Your First Investment Purchase
Have you ever looked at the list of investment options and thought to yourself “where on Earth should I start?” Getting started is easily one of the largest barriers to investing. Once you’ve made your first investment it becomes significantly easier to learn more. The key is to start with a small initial investment that will get the snowball of personal wealth rolling.
Which Stock Should I Pick?
While its true there’s a wealth of knowledge required to become a successful investor, there are simple ways to get your best bang for buck. A majority of us (myself included) don’t have the time or desire to do laborious, detailed, industry specific research. Our goal is to get from 0 to 80% with a sound investment plan. We want the other 20% to consist of fine grained details we can sweat later. For our purposes, it’s good to begin by purchasing shares of Vanguard’s Total Stock Market Index (VTSMX).
Below is a list of the top 15 holdings that make up our chosen fund. Notice any familiar names? Source: Morningstar
By purchasing a single share of VTSMX we are essentially buying a tiny slice of largest companies in America, as well as slices of mid to small companies. An example of a similar fund is the Vanguard 500 Index Mutual Fund VFINX. As you can guess, this index fund tracks the top 500 companies in America. By purchasing a share of VFINX we essentially own a piece of America’s largest 500 companies.
Index Funds vs Managed Funds
Let’s take a step back and take a look at the name of our fund: Vanguard Total Stock Market Index Fund (VTSMX). We can figure out that “Total Stock Market” means the fund tracks the entire U.S. stock market, but what does the index mean? An index fund is passively managed, meaning their portfolios have rules to track a certain group of investments and can run fairly autonomously. On the opposite spectrum are managed funds. Managed funds are actively managed by individuals which aim to outperform a certain investment benchmark.
Index funds consistently outperform any active fund for three main reasons:
- They have lower management fees
- They have a lower turnover ratio (less buying and selling of securities means less taxes)
- They aren’t prone to human error
On the topic of human error, there are plenty of poor portfolio managers out there. Experts can check portfolio manager histories before purchasing any their shares, but even then it’s possible a funding manager can switch over to another portfolio to leave them in the dust. Well managed active funds do exist. Funds with performance above their benchmarks and peers can make excellent investment vehicles, but index funds are able to consistently deliver long-term returns.
Paying Less Is Earning More
John C. Bogle is a famous investor best known for his low cost portfolios that paid off in longer time horizons. He’s been labeled as one of the “Four Giants of the 20th Century” among the investment community, and ranks among Warren Buffet, Peter Lynch, and George Soros. His investment strategy can be summarized as:
You get exactly what you don’t pay for.
He drives home how important it is to have low management fees with consistent performance over time. Manged funds have trouble consistently beating index funds over longer time horizons, because of the compounding cost of their higher management fees. Below is a graph that draws out a Vanguard Fund with a yearly fee of 0.07% compared to a fund that runs for 1.32%. The difference between their management fees is a 1.25%, which doesn’t seem like such a big deal right?
Source: theatlantic
Woah there. It turns out a whopping 27% of your potential retirement savings is lost when estimated over a 40 year period. Higher management fees bring a double whammy of shelling out cash to managers, and with it the potential earnings of your hard earned cash. To add the cherry on top, we already know that actively managed accounts have trouble beating index funds in the first place. That’s even before accounting for all the management fees!
Next Steps
While it’s possible to get excellent returns from investing in particular stocks or portfolios, a majority of people simply don’t have the time to do that level of homework. Most of us don’t need to look deep into portfolios to actively maintain it. By skipping exorbitantly high fees we can ensure simplicity while also achieving the best bang for buck. Keep an eye out for lower portfolios when investing in your 401(k) or Roth 401(k). If your employer doesn’t offer any matches, consider opening an IRA instead.
Once you’ve gotten the ball rolling towards a stable financial future, take another step. We’ve already started diversifying our portfolios with a good index fund choice, but we can also learn more about the importance of diversifying your portfolio here.
Per usual, thanks for taking the time to read BrunchBucks. If you find a typo just contact me via email! Feel free to leave a comment below or share this with friends.
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Eleaf iStick Pico Kit is a perfect combination of the innovative iStick Pico Mod and Melo III/Melo III Mini atomizer. The iStick Pico, as its name implies, is preferable, innovative, compact and outstanding. Though single 18650 cell, it can still fire your atomizer up to 75 watts and enjoy variable temperature controlled vaping experience. With the feature of top e-liquid filling design, hidden airflow control and detachable structure, the Melo III Mini is absolutely the best match with iStick Pico.
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University at Albany Libraries. Checklist of Internet Research Tips . It is not a library of evaluated publications selected by professionals. Rather, the Internet is a .
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http://www.antiochne.edu/library/researchassist/Web Research Tips
Research Tips for Searching the Internet . When you search the library databases, you're not searching the free web. The library pays for database and e-book .
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LEGEND- hyaluronate sodium injection, solution
Merial, Inc.
DESCRIPTION:
LEGEND Injectable Solution is a clear, colorless solution of low viscosity. LEGEND Injectable Solution is pyrogen free, sterile and does not contain a preservative. It is administered by intravenous or intra-articular injection.
Hyaluronic acid, the conjugate acid of hyaluronate sodium, is extracted from the capsule of Streptococcus spp. and purified, resulting in a form which is essentially free of protein and nucleic acids.
LEGEND Injectable Solution is supplied in 2 mL (20 mg) vials. Each mL contains 10 mg hyaluronate sodium, 8.5 mg sodium chloride, 0.223 mg sodium phosphate dibasic, and 0.04 mg sodium phosphate monobasic. The pH is adjusted to between 6.5 and 8.0 with sodium hydroxide or hydrochloric acid.
CHEMISTRY:
Hyaluronic acid, a glycosaminoglycan, can exist in the following forms depending upon the chemical environment in which it is found: as the acid, hyaluronic acid; as the sodium salt, sodium hyaluronate (hyaluronate sodium); or as the hyaluronate anion. These terms may be used interchangeably but in all cases, reference is made to the glycosaminoglycan composed of repeating subunits of D-glucuronic acid and N-acetyl-D-glucosamine linked together by glycosidic bonds. Since this product originates from a microbial source, there is no potential for contamination with dermatan or chondroitin sulfate or any other glycosaminoglycan.
CLINICAL PHARMACOLOGY:
Hyaluronic acid is a naturally occurring substance present in connective tissue, skin, vitreous humour and the umbilical cord in all mammals. High concentrations of hyaluronic acid are also found in the synovial fluid. It also constitutes the major component of the capsule of certain microorganisms. The hyaluronic acid produced by bacteria is of the same structure and configuration as that found in mammals.
The actual mechanism of action for hyaluronate sodium in the healing of degenerative joint disease is not completely understood. One major function appears to be the regulation of normal cellular constituents. This effect decreases the impact of exudation, enzyme release and subsequent degradation of joint integrity. Additionally, hyaluronate sodium exerts an anti-inflammatory action by inhibiting the movement of granulocytes and macrophages.1
Hyaluronate molecules are long chains which form a filter network interspersed with normal cellular fluids. It is widely accepted that injection directly into the joint pouch enhances the healing of inflamed synovium by restoring lubrication of the joint fluid. This further supplements the visco-elastic properties of normal joint fluid.
INDICATIONS:
LEGEND Injectable Solution is indicated in the treatment of joint dysfunction of the carpus or fetlock in horses due to non-infectious synovitis associated with equine osteoarthritis.
DOSAGE AND ADMINISTRATION:
Intravenously-4 mL (40 mg), Intra-articular- 2 mL (20 mg) in the carpus or fetlock. Treatment may be repeated at weekly intervals for a total of three treatments.
Strict aseptic technique should be observed when administering by intra-articular injection. As with any intra-articular procedure, proper injection site disinfection and animal restraint are important. Excess joint fluid should be aseptically removed prior to injection. Care should be taken to avoid scratching the cartilage surface with the tip of the injection needle. Diffuse swelling lasting 24 to 48 hours may result from movement of the needle while in the joint space.
For intravenous administration, use aseptic technique and inject slowly into the jugular vein.
Horses should be given stall rest after treatment before gradually resuming normal activity.
Discard any unused portion of the drug and the empty vial after opening.
PRECAUTIONS:
Radiographic evaluation should be carried out in cases of acute lameness to ensure that the joint is free from serious fractures. As with any intra-articular treatment, special precautions must be followed as to injection technique and sterility for prevention of possible swelling or infection. Intra-articular injections should not be made through skin that has been recently fired or blistered, or that has excessive scurf and counterirritant on it.
The safety of LEGEND Injectable Solution has not been evaluated in breeding stallions or in breeding, pregnant or lactating mares.
ADVERSE REACTIONS:
No local or systemic side effects were observed in the clinical field trials with either intravenous or intra-articular injections.
Post-Approval Experience: While all adverse reactions are not reported, the following adverse reactions are based on voluntary post-approval reporting:
Following intravenous use: Occasional depression, lethargy, and fever.
Following intra-articular use: lameness, joint effusion, joint or injection site swelling, and joint pain.
To report suspected adverse events, for technical assistance or to obtain a copy of the MSDS, contact Merial at 1-888-637-4251. For additional information about adverse drug experience reporting for animal drugs, contact FDA at 1-888-FDA-VETS or online at http://www.fda.gov/AnimalVeterinary/SafetyHealth/ProductSafetyInformation.
EFFECTIVENESS:
Forty-six horses with lameness in either the carpal or fetlock joints were treated intravenously or intra-articularly with LEGEND Injectable Solution in a well-controlled clinical study conducted at four locations. One, two or three injections were given based on clinical improvement. Overall clinical improvement was judged as excellent or good in 90% of the cases treated intravenously and 96% of those treated intra-articularly with LEGEND Injectable Solution.
ANIMAL SAFETY:
LEGEND Injectable Solution was administered to normal horses at one, three and five times the recommended intra-articular dosage of 20 mg and the intravenous dosage of 40 mg. Treatments were given once weekly for nine consecutive weeks (three times the maximum duration). No systemic clinical signs were observed nor were there any adverse effects upon hematology or clinical chemistry parameters. A transient, slight to mild post-injection swelling of the joint capsule occurred in some of the animals treated intra-articularly with LEGEND Injectable Solution as it did in the saline treated control horses. No gross or histological lesions were observed in the soft tissues or the surface areas of the treated joint.
HOW SUPPLIED:
LEGEND Injectable Solution is supplied in a carton of six 2 mL (20 mg) bottles.
NADA 140-883, Approved by FDA
Manufactured for: Merial, Inc., Duluth, GA 30096-4640, U.S.A.
REFERENCE:
1Swanstrom, O.G. 1978. Hyaluronate (hyaluronic acid) and its use, Proc. American Assoc. Equine Pract., 24th annual convention, pp. 345-348.
®LEGEND is a registered trademark and the horse logo is a trademark of Merial.
©2015 Merial, Inc. All rights reserved.
Made in USA
1050-8681-01, Rev. 05-2015
84671851
Legend®
(hyaluronate sodium)
Injectable Solution 10 mg/mL
MERIAL
CAUTION: Federal law restricts this drug to use by or on the order of
a licensed veterinarian.
For Intravenous or Intra-Articular Use in Horses Only
For the treatment of joint dysfunction of the carpus or fetlock in horses
due to non-infectious synovitis associated with equine osteoarthritis.
6 x 2 mL vials
Manufactured for: Merial, Inc., Duluth, GA 30096-4640, U.S.A.
NADA 140-883, Approved by FDA
|
LEGEND
hyaluronate sodium injection, solution | ||||||||||||||||||||
| ||||||||||||||||||||
| ||||||||||||||||||||
| ||||||||||||||||||||
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| Labeler - Merial, Inc. (799641006) |


If this option is selected, IntelliJ IDEA displays the Resolve Changelist Conflict
dialog on an attempt to modify a file from a non-active changelist.
Select one of the following options:
- Shelve the changes (see Shelve
changes for more information).
- Move changes to active changelist.
- Switch to changelist:
the current changelist will be made active.
- Ignore:
the file will be added to the list of files with ignored conflicts.


UNIVERSAL PICTURES PRESENTS AN ALPHAVILLE PRODUCTION OF A STEPHEN SOMMERS FILM BRENDAN FRASER "THE MUMMY" RACHEL WEISZ JOHN HANNAH ARNOLD VOSLOO JONATHAN HYDE KEVIN J. O'CONNOR SPECIAL VISUAL EFFECTS AND ANIMATION BY INDUSTRIAL LIGHT & MAGIC MUSIC BY JERRY GOLDSMITH CO-PRODUCER PATRICIA CARR FILM EDITOR BOB DUCSAY PRODUCTION DESIGNER ALLAN CAMERON DIRECTOR OF PHOTOGRAPHY ADRIAN BIDDLE B.S.C. EXECUTIVE PRODUCER KEVIN JARRE PRODUCED BY JAMES JACKS & SEAN DANIEL SCREEN STORY BY STEPHEN SOMMERS AND LLOYD FONVIELLE & KEVIN JARRE SCREENPLAY BY STEPHEN SOMMERS DIRECTED BY STEPHEN SOMMERS
Universal Pictures, once was the bastion of the Horror genre. Such venerable American icons as the Frankenstein Monster, Count Dracula, Lawrence Talbot, the Wolfman, and in the 50's, The Creature; have scared and endeared themselves to the movie going public. One of Universal Pictures most maligned creations, Karis, the Mummy; was relegated to the moderate level of 'B' picture. Having no other purpose that to come back to life to aid a conniving Egyptian Priest in his attempt to preserve their riches. All this while Karis himself tried vainly to recapture the reincarnated Princess Annunka. While these films were enjoyable programmers, they were never considered classics and rightly so. The only film to gather any notoriety was the original 1932 version of "The Mummy" which stared the venerable Boris Karloff in the role of the dreaded Imhotep. In this new appraisal of the classic tale, Director Stephen Sommers has created a sometimes amusing, but mostly barren piece of fluff.
In a nicely constructed prologue, we witness the murder of the Pharaoh by his Mistress, Anck-Su-Namun and her lover, the high Priest Imhotep. Imhotep's lover sacrifices herself so that the Priest can escape and through his magic, return her spirit to her body. Unfortunately, the Pharaoh's guard interrupt the proceeding and mummify the Priest in a dreaded procedure that has Imhotep buried alive in a sarcophagus full of flesh eating beetles.
Flash forward to the 1920's. Rick O'Connell (Brendan Frazer) is fighting off an army of unidentified Arabian fighters. As the army decimates his men, O'Connell outruns the hordes until a huge statue corners him. His pursuers suddenly turn and run away. O'Connell turns and sees that he is standing beneath the statue of Anubis, the resting place of Imhotep. The sands move and erupt, taking on the appearance of the dreaded Priest, threatening to swallow O'Connell. He escapes and wanders the desert. Finally captured and placed in a Moroccan jail, he waits for his execution.
To his rescue, comes Evelyn (Rachel Weisz) and her brother Jonathan (John Hannah), who discover that O'Connell may know the location of the fabled city of the dead, Hamunaptra. The expedition begins. O'Connell and Evelyn develop an uneasy romantic relationship, as they uncover Imhotep's tomb. Unfortunately, another troupe of treasure hunters led by actor Jonathan Hyde, follows them. Finding the tomb, Imhotep is released from his slumber and proceeds to murder those who opened his crypt, taking their organs for his own use. Imhotep soon kidnaps Evelyn to use as a sacrifice to the old Egyptian gods, in preparation to brining his beloved Anck-Su-Namun back to live.
The plot, as described above is pretty much, all there is to the film. There are several other developments and sequences that deserve attention, but they are of fleeting entertainment value. Suffice to say that, there are moments of good fun in this film, but not enough to warrant the 124 minute running time. At this length, the film seems overly long. The script is wont with contrivances and plot holes. Once Imhotep starts to wander about Egypt, the special effects take center stage. While there are some very good visuals in the film, I had the strange feeling that I was actually watching an old Charles Schneer production. Scenes of the computer generated mummy, it's skeleton visible through sections of it's anatomy (imagine the Aurora model kit of Grey's Anatomy, with some gauze raped around it and you'll get the picture). Unfortunately, I kept being reminded of Ray Harryhausen's Dynamation effects, rather than any sort of dread that these scenes may have had. While the effects are quiet enjoyable and stunning, they don't have that fission , that 'oh my gosh!' quality that drives this kind of picture.
The performances are nothing special in the film. Brendan Fraser does an okay job of filling in the matinee idol part. He fills the 'Indian Jones' role quite well in fact. Mr. Fraser is one of those rare actors that seems to enjoy the absurdity of the project he is involved in. A refreshingly joyous quality. However, his climatic fight with horde of mummified warriors introduces lots of Hong Kong Wu-Shu type martial arts, which are completely incongruous with his character and the setting. I don't think most westerners had the opportunity to study under the Shaolin monks at that time in history. But, this is a fantasy, so it's forgivable to an extent.
Rachel Weisz brings to her character of Evelyn a nice dry wit. Her comic timing is very refreshing. Moreover in a film being marketed as a horror / adventure, the character should be a least a little frightened. John Hannah as Evelyn's brother, Jonathan is completely wasted. His character does nothing but whine and comment on the proceedings, only occasionally getting involved in the action. One of the most interesting and ultimately most frustrating casting notes in the film is the inclusion of Bernard Fox as the aging and slightly mad Corporal Wilson. The character brings a welcome bit of comic relief to the film that is not derived from the constant one-liners and slapstick situations that abound in the film. Unfortunately, the character is used solely as a device in which to get the characters to their destination after which, he is conveniently killed off.
Kevin O'Connor is creepily amusing in his part of the cowardly, Beni. His confrontation with Imhotep, wherein he tries to protect himself from the approaching monster by holding up several holy artifacts he wares around his neck. When he finally holds up the Star of David, Imhotep recognizes the symbol as belonging to the Slaves of his time, and takes the former guide as his accomplice promising him untold riches. Unfortunately, Mr. O'Connor is afforded much too much screen time, and consequently, the character becomes less and less threatening, finally ending as a buffoon.
Arnold Vosloo is actually quiet good in the role of the re-constituted Priest Imhotep. Having appeared opposite Lance Henrickson in John Woo's "Hard Target" and the two 'Darkman' sequels, Vosloo carries with him an appropriate air of menace and stature that are a welcome benefit to the film. Vosloo is a natural screen villain. His charisma is that good.
The script for "The Mummy" is comic book from start to finish. While there is nothing wrong with that, what the script lacks is a sense of dread that any horror film needs to have. All the adventure and action in the world has no value without a sense of danger. Since none of the characters express any sense of jeopardy, it is hard to feel involved in the proceedings. While the film is enjoyably kinetic, it would have benefited from a little more suspense and less visual buffoonery.
Also, there are the dreaded instances of contrivance throughout the film. The inclusion of the flesh eating beetles as part of the mummification process in the prologue of the film is a nice and creepy touch. Yet having the pests remain alive after 3000 years of starvation (not much to chow on in a sealed crypt), is a bit of a stretch. Furthermore, these beetles continue to crawl all over Imhotep after he starts to replace his organs, the bugs crawling in and out of his flesh. Why these bugs just don't eat the darn guy up again is something that I cannot explain. A quick thanks should also go to the prop managers who managed to leave a convenient assortment of camels at the tomb for our heroes to escape on when the entire city of the dead collapses in on itself.
As it stands, "THE MUMMY" is a fun time waster. There is nothing in the film that will linger after you've left the theater. And the possibility of a franchise is moot at best. It should be mentioned that there are scenes of a grotesque nature in the film that may not be acceptable for younger viewers. The PG-13 rating is an accurate one.
Copyright © 1999 R. L. Strong Nothing in this article may be reproduced or altered in any way without the expressed written permission of the author..
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SimplePipe will only extend exactly the elements requested (from what is
availble), so it can be used for testing with a static buffer to simulate
data coming in at any rate.
The upstream data. This can be any iopipe. Throughout the library, the
upstream data is generally saved as a member called "chain" as a matter
of convention. This is not required or expected.
Get more data from the pipe. The parameter indicates the desired number
of elements to add to the end of the window. If 0 is passed, then it is
up to the implementation of the pipe to determine the optimal number of
elements to add.
Release the given number of elements from the front of the window. After
calling this, make sure to update any tracking indexes for the window
that you are maintaining.
Get the current window of elements for the pipe. This is the data that
can be used at this moment in time.
Example
// any array is a valid iopipe source.
auto source = "hello, world!";
auto pipe = SimplePipe!(string)(source);
// SimplePipe holds back data until you extend it.
assert(pipe.window.length == 0);
// Note: elements of narrow strings are code units for purposes of iopipe
// library.
assert(pipe.extend(5) == 5);
assert(pipe.window == "hello");
// Release data to inform the pipe you are done with it
pipe.release(3);
assert(pipe.window == "lo");
// you can request "some data" by extending with 0, letting the pipe define
// what is the best addition of data. This is useful for optimizing OS
// system call reads.
assert(pipe.extend(0) == 1);
assert(pipe.window == "lo,");
// you aren't guaranteed to get all the data you ask for.
assert(pipe.extend(100) == 7);
assert(pipe.window == "lo, world!");
pipe.release(pipe.window.length);
// this signifies EOF.
assert(pipe.extend(1) == 0);


Before Change
// Test streamlines with mixed dtype
streamlines_mixed_dtype = [streamline, streamline.astype(np.float64), streamline.astype(np.int32), streamline.astype(np.int64)]
nb_points_mixed_dtype = map(len, dipystreamline.set_number_of_points(streamlines_mixed_dtype, nb_points))
assert_array_equal(nb_points_mixed_dtype, [nb_points]*len(streamlines_mixed_dtype))
// Test streamlines with differente shape
modified_streamlines_cython = dipystreamline.set_number_of_points(heterogeneous_streamlines, nb_points)
After Change
// Test streamlines with mixed dtype
streamlines_mixed_dtype = [streamline, streamline.astype(np.float64), streamline.astype(np.int32), streamline.astype(np.int64)]
nb_points_mixed_dtype = [len(s) for s in dipystreamline.set_number_of_points(streamlines_mixed_dtype, nb_points)]
assert_array_equal(nb_points_mixed_dtype, [nb_points]*len(streamlines_mixed_dtype))
// Test streamlines with differente shape
modified_streamlines_cython = dipystreamline.set_number_of_points(heterogeneous_streamlines, nb_points)


Type sudo ufw status. This will tell you the current status of the firewall.
Ideally, the firewall should be on. If the previous command returns Status: inactive, then you'll need to enable the firewall. To do this, type sudo ufw enable.
That's it! you can verify that the firewall is on by typing sudo ufw status again.


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EB APIs
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Feature Demo Tab 2


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THE TEN COMMANDMENTS
Drama (USA, 1956)
Starring Charlton Heston, Yul Brynner, Anne Baxter, Yvonne De Carlo
Directed and produced by Cecil B. DeMille
"The Ten Commandments" was a great epic when made in the fifties, and it continues to enthrall the audience today. I saw it on a small TV-screen, and most certainly wished I could have watched it on the big screen.
The script follows the Bible to some extent, but also draws in non-biblical material, such as novels, which enhances the overall movie. (The Bible is a bit secretive about Moses' life until he reached 30.)
The old-fashioned epic style, impressive and visually stunning as it may be, isn't always as appealing to the modern movie-goer. Firstly, the movie is well too long and moves a bit slow at times. Then there is a lot of over-stylized acting, and although Charlton Heston is brilliant as Moses, he may be a little pompous at times. But he is a more powerful and impressive Moses than the animated character of the recent release "Prince of Egypt". Except for the snake scene at Pharaoh's Court, the effects are enjoyable. "Prince of Egypt" does a more 'realistic' Red Sea parting with modern computer-generated animation, but "The Ten Commandments" did it the best way possible in those days, and it's still impressive to watch.
As mentioned, the acting tends to be overdramatized, and this is a left-over bone from the days when silent movies ruled the cinemas. It must however be noted that Anne Baxter as Nefretiri and Yul Brynner as Rameses both do excellent performances, and Cedric Hardwicke as Pharaoh Sethi is convincing, and even humorous (his belated death scene).
Some of the love scenes, which function merely as subplots, do little more than wear out the audience, as we all are very anxious to get Moses going. After all, we do know the story. The ending is better than that of "Prince of Egypt"; we are here getting to the point where Moses appoints Joshua to lead his people into the promised land.
I rate this movie 5 on a 1-6 scale.
Stian M. Landgaard, February 3rd, 1999
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You can import the blocks presented here as a project or download them as a library to import into your projects.
This chapter of the guide relies upon a library for machine learning that are known to run well in the Chrome or Edge browser. See the troubleshooting guide for how to deal with problems encountered.
In previous chapters you used blocks that relied upon pre-trained machine learning models. In some cases, using transfer learning you were able to extend the models a little. In this chapter we introduce blocks that enable you to create deep machine learning models from scratch, train them, and use their predictions in your projects.
One can build models that learn about real-world data such as weather, traffic, astronomy, economics, politics, and any of the many other open data sources. You can build models that will try to guess the next number in a sequence.
Neural nets can only process numeric data. This isn't a great limitation however since we provide blocks that convert text, images, and sounds into numbers:
As we explore later in this chapter one can create intelligent game playing programs using these machine learning blocks. The trick is to turn game boards/positions into a list of numbers and the game outcome into a number so the model can be trained on records of previous games.
To create and use a machine learning model you need to:
In the following you can use the Create a neural net model ... block to create a model with two "hidden" layers of size 100 and 20. (Hidden here means that they are in-between the input and the output.) You can then use the Send training data ... block to set the training data to ten numbers and their square roots. Next you can use the Train model named ... block to train the model. This can take several seconds and you should finally see the remaining "error" (often called the "loss"), the accuracy, and how long it took. Smaller errors are better. Finally, you can use the Get predictions from model ... block (the version that gets many predictions at a time) to test how well the model has learned.
You will notice that it is hard to train the model to compute square root estimates very well. There are full-featured versions of blocks for creating and training models that enable you to alter more parameters. We suggest you first experiment with what are called the hyperparameters such as the number and size of the layers, the optimizer, the learning rate, the loss function, the validation split, and the number of learning steps. With the right settings the following can create a good square root estimator.
Note that the training can take several minutes. So long as you leave the tab visible you can do other things on your computer while it is running. We advise putting the tab in a new window. Once a model is trained and you are ready to use it in your app you can save the trained model and load it into your app to avoid the training time.
By changing the input and output lists you can make a model that computes different functions. For example, you could train a model to convert from Centigrade to Fahrenheit. Try a few functions and see if some are easier to train than others.
The machine learning blocks are not restricted to learning how to map one input number to an output number. A simple example is a model that estimates the distance a point is from the origin. Given values for x and y it estimates the square root of the sum of the squares of each.
You can train a model to put a label on data. For example, here is a model trained to provide a name for a color. It was trained to output a label for a vector of three numbers: hue, saturation, and brightness. 50 different random colors were given one of nine color names. Instead of responding with a number or a color name the model lists a "score" for each of the possible color names. The scores add up to 1. The confidence or probability of being correct of each label is turned into percentages by multiplying each score by 100. The following model does well with only a few colors, perhaps you can improve by training it with more colors and by training it better.
Neural nets only work with numbers so how do outputs such "red" and "green" work? Click to learn how.
The Snap! blocks for machine learning check if the output values are numbers. When they aren't, it collects together all the unique values (e.g., all the color names in the data). It then uses a technique called one hot. If the colors were "red", "green", and "blue" they could be converted to 0, 1, and 2. But it wouldn't work well since the numbers imply that "red" is closer to "green" than "blue". Even if you think that is OK what about when one adds "white" or "black" to the list? With one hot, red can be <1, 0, 0>, green <0, 1, 0> and blue <0, 0, 1>. Each color is equally distant to any other this way.
The model responds with predictions with a vector of numbers between 0 and 1 that add up to 1. Each number is what the model estimates is the probability that the correct color name is the corresponding one in the list of color names. E.g. For if the prediction is <0.5, 0.2, 0.3> for "red", "green", and "blue" then the model is estimating that the odds of red being correct is 50%, green 20%, and blue 30%. The Snap! model prediction blocks return a list where each element is a list of a color name and number. The Name this color ... in the above sample project sorts the list by the probability scores and multiplies by 100 to convert to percentages.
One way to draw a polygon is to repeatedly go forward and turn. E.g. a pentagon is
If we add a little "noise" to the amount that the sprite goes forward or turns we get a bit more interesting result.
If we change the pen colour and size each time we can get some very pretty results.
Maybe we can train a neural network to rate how nice noisy polygons look. In the following after giving some random noisy polygons ratings you can train it to predict ratings. Each noisy polygon is defined by just 12 numbers: minimum step, maximum step, minimum turn, maximum turn, minimum red, maximum red, minimum green, maximum green, minimum blue, maximum blue, minimum pen size, and maximum pen size. The neural net will be trained with a collection of these 12 numbers as input and rating numbers as output.
One way to build a question-answering app is to use a table of expected questions and known answers. To deal with the many ways to ask the same question one can train a model using the features of sentence block described in the chapter on word and sentence embeddings. An app can respond to questions by choosing the top-scoring predictions from the model given the embedding of the question. Here is a project for defining, training, and testing such as app:
Using the features of sentence block together with a few training examples we can create an app that will indicate the degree of confidence (or lack thereof) indicated by the input sentence. Here is a project for defining, training, and testing such as app:
Here is simple project that uses this model.
A deep neural net consists of an input layer, some number of hidden layers, and an output layer. The nets that the Create a neural net model ... block creates are always in a sequence and each neuron is connected to every neuron in the neighboring layers. While this is a common architecture, there are many models that connect the neurons in other patterns. Some contain branches and loops and some are connected more sparsely.
When predicting each neuron adds up all of its inputs from the previous layer after multiplying each input by a weight. Instead of sending the sum out to the neurons of the next layer it first checks if the value is negative and then sends zero instead. (This is a popular operation on the sum but some neural net models do other mathematical operations instead.) It is the weights (sometimes called "parameters") that are "learned" during training. They do this using a mathematical technique called gradient descent. It repeatedly takes small steps to reduce the "loss" or "error". There are different strategies for doing this efficiently that are called optimizers. The size of the step taken each time is called the "learning rate". If the learning rate is too small progress is very slow. If it is too large then the error may bounce up and down rather than making progress towards a lower value.
The get layer of model block can be used to obtain the parameter values for any layer of a model. Also, the open support panel tensorflow.js block can open up the model interface page which has a section called "Model after creation" or "Model after loading". In that section one can obtain statistics about each layer.
Models and data can be saved and loaded using the 'Saving and loading' button which can be found by clicking the open support panel tensorflow.js block to open an interface page and navigating to the Save/Load section. Your projects can load models using the Load neural net from URL ... block if you have copied your saved model files (both the JSON that defines the model and the learned weights file) to a web server.
Other blocks can load data using the Load X data from URL block where X can be either "training" or "validation". Training data is used in training both as data to process and a random sample is used to calculate how well the model is doing. Validation data is used to calculate the error or loss during training. The validation data error can be used to tune the learning process.
Every day there is news of new applications of machine learning to problems. Sometimes it is for entertainment as when SnapChat filters add animations or distort people's faces. Sometimes it saves lives as when students used machine learning to predict wildfires. Or to generate art that sells for a high price or to help Japanese cucumber farmers sort their cucumbers. Machine learning is increasingly used to help scientists, for example, to find exoplanets.
You can bring data into Snap! and use it to train your models. You can find available data by doing a web search but often even better is to use Google dataset search. In the following we found two datasets to explore whether weather data can help predict incidence of influenza (you may wish to explore covid and weather instead). For a collection of daily weather records for hundreds of places worldwide, some going back more than one hundred years, we used this weather data (documented in the readme.txt file). And for the number of cases of flu each week across many countries we used Epi Data Surveillance Information from the World Health Organization.
An easy way to bring data into Snap! is by dragging a file onto the Snap! browser tab. This works for TXT, CSV (a spreadsheet format), and JSON (JavaScript structured data) file types. Or if you already have a variable, you can import its contents by right clicking on the variable's "watcher" and select import.
Another way to bring data into Snap! is via the url reporter. Many data sources provide URL-oriented interfaces that report JSON or CSV data. The split reporter can interpret JSON and CSV texts into data structures implemented as lists of lists of lists of ...
Bringing data into Snap! via a URL (or more generally using the GET and POST methods in Snap!'s Web Services Access library) has the advantage of providing the most up-to-date data to your apps. It also makes your projects much smaller since the project doesn't need to have variables holding the data. But it is only practical when users have fast and reliable Internet connections.
Bringing data into Snap! as text is sometimes only the first step. If you import some text and need lists, you can use the Snap! split ... by block to turn text into lists. Data often needs to be cleaned up and restructured. This is often called data wrangling.
In this Snap! project data wrangling is performed on both the weather and flu data. The idea is to have each element of the weather dataset to contain the weather for a week matched up with the flu data for the following week. (If one week is not a good estimate for the duration between exposure and confirmed diagnosis, then one can try other offsets.) One problem is that the flu data from the World Health Organisation is weekly for entire countries while the weather data is daily for weather stations (often in cities). In the sample project data for Ireland and Switzerland were used because a big country could have very different weather in different parts. Also one needs to avoid datasets that are incomplete which occur often.
Weather and flu are seasonal so to make interesting predictions we need to determine whether the weather data having been higher or lower than typical for that time of year predicts how the flu cases will vary from what is typical for that time of year. To address this, an earlier version of the sample project changed all the data by dividing by the average values. This worked fine for the flu data since the data is always non-negative. The flu data is capturing the multiplicative factor to which the case numbers were above or below average. This did not, however, work for the temperature data since the values can be negative. The mean temperature can even be zero which cannot be used as a divisor. Furthermore, some of the values were large enough that sometimes early in training the loss became NaN (not a number). This stops the training due to some internal model parameters becoming too big or small. The new version of the project uses the normalize block to instead map all the temperatures by subtracting by the mean and dividing by the standard deviation to normalize the data. The weather data were normalized for the averages for each month of the year. This improved the model's predictions (but they still were only a little better than predicting the average values).
Another challenge is to work out how to evaluate how well a model does. Is it good enough if the model predicts better than chance given a week's weather data whether the next week's flu reports will be above or below average? Or should we measure how far off the predictions are on test data from what the following week's flu occurrences really were?
If you want to work on the question of whether the weather partially helps predict flu cases in the following week then there are many things to try to enhance this first draft of such a project. Perhaps you can improve it with more or different data. Or by designing a different machine learning model and training scheme. Or if you start to get good predictions then see if a simpler model works almost as well. Maybe the precipitation measurements don't matter. Or maybe it doesn't need the full daily measurements of the previous week. Alternatively, try doing something similar with other available datasets. There are excellent covid datasets, for example.
Tic Tac Toe (also known as Noughts and Crosses) is a simple game. A computer could have all 255,168 games in memory and play perfectly by searching its memory. A Tinkertoy computer made of wooden spools and sticks plays a perfect game. So does a computer made of DNA.
Donald Knuth, a world famous computer scientist, wrote a Tic Tac Toe learning program in 1958 when he was 20 years old. The computer had 10 kilobytes of memory (the laptop that I'm using to write this has more than one million times more memory). In those days computers didn't have keyboards, displays, or mice.
Here is a five-minute video where Donald Knuth tells stories of his Tic Tac Toe program:
Your challenge is to train a deep neural net to play well. You can start with this Snap! program for playing Tic Tac Toe and add scripts for recording games, creating neural nets, using those games for training your nets, and finally using the trained neural net models to play the game. This is quite challenging and only appropriate for those with a good deal of experience with Snap! and machine learning.
An alternative is to instead focus on learning how to design good neural nets and how to train them effectively. Click on the following to skip the Snap! Tic Tac Toe programming challenge and instead start training models right away. Once you have a model trained to win (most of the time), you can set up tournaments with the trained models of classmates or friends!
Click to see how you can train a neural net to play Tic Tac Toe in Snap!. You might want to try this on your own before reading this.
This Snap! program has a variable called Players whose value is a list of two player types. A player type can be 'human', 'random', or the name of a model you created. Games are recorded and are the basis for training. The space bar will start a new game and, if humans aren't playing, a new game will start as soon as a game is over.
There is a supporting web page that allows you to experiment with training and provides a way to save and load models and game data. Use the open support panel tensorflow.js block to open up this page. It will have a 'return to Snap!' button.
The core idea is that the board positions are recorded after each move. When a game ends then outcomes are associated with each move. If the game ended in a tie, the outcome is 0. If the move led to a win, then 1, otherwise -1. The model is trained to predict a value between -1 and 1 for board positions resulting from moves. Note that it needs to predict the outcomes of board positions, even if it never encountered the board during training.
Neural nets work only with numbers so we need to decide how a board position will be represented with numbers. A straight forward way would be to use a vector of nine numbers for each board position. 0 could mean empty, 1 X has moved there, and 2 O has. This works but not as well as the following scheme. The problem is that by mixing things of different kinds (moves and non-moves) the relationships between boards is hard to discern. For example, the difference between the upper left corner having O instead of X is 1 but so is the difference between that position being empty and having an X. And why should the difference between an empty square and an O be twice the difference between an empty square and an X? So to avoid this problem machine learning programs typically use one hot vectors that are all zeros except for a single one. So X can be <1,0,0>, O can be <0,1,0>, and empty can be <0,0,1>. While a board could then be a 9x3 (or 3x9) matrix it is easier to flatten the structure so that every board is a vector of 27 zeros and ones.
Once a model has been trained the predictions for each possible move can be computed. There are at least two ways use these predictions to make moves:
The best advice is to experiment! No one knows what is the best model architecture, the best way to generate good training data, how much training is required, etc. One way to search for a good architecture is to create two models with a different number of layers or differences in the sizes of their layers. After training them both with the same data, have them play a large number of games and see if one wins more than another. Keep the good one. (You can save models after running the open support panel tensorflow.js block.) Repeat with a different model architecture for the weaker player. Similarly, you can explore different training parameters to see which optimizer or learning rate works best. Try to discover the number of learning steps after which very little progress is made in reducing the loss so you don't waste time. One way to detect if a model benefits from additional training is to have an older version play against a newer version of the same model.
You can do all these experiments using the Snap! program but it takes several seconds to run a game. For running many large-scale experiments we provide this web page for Tic Tac Toe machine learning. It can run thousands of games in a few seconds for evaluating models or generating lots of training data. Note that you can save a model created in Snap! and then load it into the Tic Tac Toe page and vice versa.
Not all training data is equally useful. Probably the least useful is random move player against random move player. But it is easiest to generate. A human playing another human is likely to produce very good data. One way to generate lots of good data is to create a Snap! script for an expert player and use records of its games for training.
It is hard to know what parameters to use to define a neural net model and its training. How many layers? How many neurons per layer? How many training steps? How large should each training step be? Which activation and loss functions to use? What is the best optimization method?
One can run lots of experiments to find better and better answers to these questions. The answers are called "hyperparameters" because they are not the weights/parameters of the neural net but instead control how those weights are determined. Recently researchers have begun to write programs that automate this search for good hyperparameters. This is often called AutoML, short for automated machine learning. These programs can search for hyperparameters that make the best quality predictions or classifications or hyperparameters that are small and fast but still pretty accurate.
The Search for good neural net model parameters block can be used to search for hyperparameters that minimize the "loss" (make better predictions). You can control how many experiments it should run. Explore the following to see how well a model can be trained to estimate the length of the hypotenuse of a triangle.
Here are some project ideas:
Several enhancements to this chapter are being considered. There are many model architectures that cannot be expressed. Convolutional networks are very successful at handling image and sound input. Recursive neural nets handle time-based data well. Transformers are increasingly used for language models and multi-modal data. Reinforcement learning enables unsupervized learning (using only input datasets and in-world or in-simulation rewards).
This chapter provides a simplified interface to TensorFlow.js, a JavaScript implementation of Google's TensorFlow machine learning library. There is a set of tutorials for learning how to use TensorFlow.js (some familiarity with JavaScript required). Stephen Wolfram wrote a Wolfram Language textbook chapter and a blog post about machine learning for middle and high school students (some familiarity with Wolfram Language/Mathematica required). A high school student wrote this guide to learning machine learning. In this chapter we have focused on the high level "layers" part of TensorFlow.js that is closely based upon Keras (some familiarity with Python required). There are good articles about training optimizers, loss functions, and different uses of training, validation, and test data.
You can import the blocks presented here as a project or download them as a library to import into your projects.
Go to the next chapter on K Nearest Neighbors.
Return to the previous chapter on word embeddings.


Protein Interaction Network Analysis Using Cytoscape and NDEx
In this tutorial, you will:
Generate a protein interaction network from tabular interaction data.
Extend the network with protein interactions from public resources.
Format the network for effective visualization.
Open networks from the web in Cytoscape.
Save networks to NDEx.
Background
The data used for this protocol is from an affinity purification-mass spectrometry experiment which identified interactions between human and HIV proteins by Jäger et al. In this quantitative AP-MS experiment, a relatively small number of HIV bait proteins are used to pull down a larger set of human prey proteins.
Note: This tutorial does not describe how to pre-process the raw AP-MS data, the data used here is already scored and filtered.
Note: You can also choose to skip the loading of the data to generate an interaction network, instead downloading a previously created network from NDEx.
Use the App Manager to update all Apps to the latest version available.
Option 1:Create the Interaction Network from Tabular Data
Load the network via File → Import → Network from File...., and select the Example data.
In the Import Network from Table dialog, select the Bait column as Source Node and Prey column as Target Node.
Designate the AP-MS Score as an Edge Attribute.
For all other data values, designate them as Target Node Attribute, by clicking in the column header and selecting the red spreadsheet icon.
Click OK to import the network.
Option 2: Import the Interaction Network from NDEx
Search the AP-MS Demo Data network in NDEx by typing "ap-ms demo data" in the NDEx Search Box and clicking the search icon .
The cyNDEx Browser window will pop up... Click the Import button next to the AP-MS Demo Data network, then close the pop up window.
The Interaction Network
Cytoscape will now display the interaction network with a default style and a grid layout (When loading from tabular data as described in Option 1, the layout may be different depending on your Cytoscape settings. Don't worry, this will not matter.)
Add Human PPI to the Network
Now extend the network by adding known protein-protein interactions between the human proteins. This interaction data will be obtained via the NDEx Integrated Query (IQuery) using the human prey proteins from our network.
Click the Filter tab in the Control Panel
Click the "Plus" sign and add a Column Filter
Then, use the Dropdown element to select the Uniprot column
Now, select all the genes in the name column by selecting the top item, then scroll down and hold down Shift and then selecting the last entry.
Click Ctrl+C (Command+C if using a Mac) to copy.
Note: there is a minor issue with selection in which all of the columns are highlighted, not just the one that is selected. While confusing, the selection works correctly.
Paste the list of gene symbols into the iQuery text box
Click the search icon to run your query
Add Human PPI to the Network
In the following results screen, select the Protein Interactions tab to view results from several public sources available in NDEx. We will use the result of querying HumanNet - XN, pre-filtered to contain only interactions with a LLS score of 3.5 or greater ("LLS" is a score specific to HumanNet).
Select the HumanNet - XN result.
Click the Cytoscape icon in the top right portion of the screen to load the result in Cytoscape.
Add Human PPI to the Network
The IQuery result is automatically imported in Cytoscape and the resulting network, named Direct query result on network - HumanNet - XN (LLS >=3.5), contains known interactions between human query (prey) proteins, with a LLS score of 3.5 or greater.
Preparing to Merge Networks
Before merging our two networks, we should clear the Column Filterthat was applied previously. To do so:
Select again the AP-MS network
Click the Filter tab in the Control Panel
Click the "X" sign next to the Column Filter to remove it
Merge Networks
Now we are ready to incorprate the human protein interactions from the IQuery result: we are going to merge the AP-MS network with the HumanNet XN query result, matching nodes using the official gene symbols stored in the name column.
Go to Tools → Merge → Networks...
In the Available Networks list, select both networks and click the right arrow to add them to the list of Networks to Merge
Expand the Advanced Options interface
In the Matching Columns field, select name for the AP-MS network and name for the IQuery result network if they are not already pre-set
Click the Merge button
The Merged Network
When the merged network first loads, it will have the Cytoscape default style (inherited from the AP-MS network) and the Cytoscape default layout (Prefuse Force Directed Layout). All nodes look the same and this makes it difficult to interpret.
Visual Styling Based on Continuous Numeric Data
Let's now improve the visualization of our merged network by creating continuous mappings to style nodes and edges based on the data associated with their properties. Note: if you are not familiar with Continuous Mapping of data to visual styles in Cytoscape, please review this short (1 min) video before moving forward to the next section.
Visual Styling Based on Continuous Numeric Data
To begin, we will color the nodes based on their Jurkat Score property (associated to all human query nodes); then we will set the width of the edges based on their AP-MS Score property:
In the Style tab of the Control Panel, create a new style named AP-MS Jurkat Score.
Create a continuous mapping for the node Fill Color using the JurkatScore column, then select a purple gradient from the ColorBrewer palettes.
Switch the deafult node Fill Color to yellow to highlight the HIV proteins.
Create a passthrough mapping for node Label using the name column.
Now switch to the Edge tab and create a continuous mapping for edge Width using the AP-MS Score column and set the width values to a range from 1 to 10.
Visual Styling Based on Discrete Data
Let's now further enhance our network's visualization by applying a discrete mapping to better differentiate its edges. Note: if you are not familiar with Discrete Mapping of data to visual styles in Cytoscape, please review this short (1 min) video before moving forward to the next section.
Visual Styling Based on Discrete Data
We will now create a discrete mapping and color the edges to distinguish HIV-Human interactions from Human-Human interactions:
Create a Discrete mapping for the edge Stroke Color (unselected) property using data from the interaction column
Choose a GREEN color for all interactions of type interacts with
Then change the default property color to RED
The Improved Visualization of the Network
The visualization of the network now highlights the AP-MS experimental data (GREEN edges), as well as additional known interactions from HumanNet (RED edges), with a PURPLE gradient node color indicating the Jurkat Score for our human query genes.
Inspecting Visualized Interactions
Zooming in on the network, specifically the POL and PR HIV nodes, we can see a set of interactions found in the original paper by Jäger et al, which represent binding of POL and PR with the translational initiation complex EIF3.
Pathway Enrichment with NDEx IQuery
IQuery lets you find pathways relevant to the host (human) proteins. Go back to the IQuery web page and select the Pathway Enrichment tab to view a list of relevant pathways from several sources.
Pathway Enrichment with NDEx IQuery
There are three ways to sort the enrichment query results, selected by the sorting menu:
By default, pathways are sorted by the Similarity method
Use the sorting dropdown menu to choose the Overlap or p-Value methods and the order of the results will change
Click on the Info Icon next to the sorting dropdown menu to see the documentation for each sorting method.
Each enriched pathway shows its 3 scores and the one currently used for sorting is shown in bold
Open in Cytoscape from IQuery
Select the 5th result GO:0006413 (translational initiation), then click the Cytoscape icon to open the network in Cytoscape as you already did earlier on.
IQuery has added a column called querynode to nodes in the network to indicate that these are the proteins in the network that match your query. You can use that information to highlight those nodes.
Replace the existing Discrete mapping for node Fill Color to instead use the querynode column
Choose a BRIGHT GREEN for the case where querynode is true to distinguish the cluster of EIF3 family proteins that are in common between the original AP-MS network (human prey proteins) and this GO:0006413 (translational initiation) pathway.
Save to NDEx from Cytoscape
From Cytoscape, you can easily save the modified pathway network directly to your NDEx account.
To do so, select the GO:0006413 (translational initiation) network in the Network Tab of the left hand panel.
Click the button in the top tool bar, then choose Export Network to NDEx
The NDEx Save Dialog will be displayed, but the Export Network to NDEx Button in the lower right corner will be deactivated (red oval) because Cytoscape is not yet signed in to an NDEx account.
Click on the account manager box in the upper right hand corner and choose Add new profile...
Enter your NDEx account name and password, then click the Sign In button. The Export Network to NDEx button will now be enabled allowing you to complete the save operation.
Save to NDEx from Cytoscape
Now visit the NDEx website at www.ndexbio.org and log in to your account
In your account page, you will see the GO:0006413 (translational initiation) network that you just exported from Cytoscape at the top of the list.


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All it takes is an SMS from your registered mobile number to get your vote counted. The security and reliability of elections do matter. And so does each and every vote. What is better than bringing both at once?
Not everyone has a smart phone, forget those with an active internet connection. But there are the basic mobile phones that people use. By creating a technology that is built to work perfectly on any mobile phone, this application will be very easy to bring into practice.
Everyone has to vote and everyone has a phone. What else other than smsdelfva can bring all eligible votes into account and make the largest elections of the planet a lot easier. No matter where you are, what you do is just sms your vote and smsdelfva makes it count.
Register your phone with your voterID and you are good to go. Nothing else is needed from you. Not a day spent at the polling booth. Not the blue ink blot on your nail. Not even an internet connection. Just a plain simple SMS to vote.
With the Azure Blockchain to provide the secure voting platform and the sms system from Twilio, there is nothing to worry about. smsdelfva takes voting right into your hands, in your mobile phone. We are that serious about you having to vote.
SMS is the link between every eligible voter and all that Azure Blockchain has to offer for the elections. Leave no man behind, for this election. And we are dead serious about it.
It has to be done almost like an election on a single day with the blue ink blots to prevent people from linking their counterfeit voterIDs with fake phone numbers. Once this logistical nightmare is done with, all that remains is an SMS notification about what has to be done by the voters.
The hardest part has been done. Now, all that remains is let the voters know of the election dates. smsdelfva can be used to get the list of candidates for the election along with the facility to vote.
smsdeflva uses the Twilio portal to make the SMS hit the correct endpoint and get the desired action to be performed. We have load balancers configured in Twilio anticipating the demand surges during the election proceess.
Now the Azure Blockchain takes care of the safety and security of the votes. It also has checks for the voting date and also sends an alert to those who haven't voted yet. This makes sure that no one gets left, with smsdeflva.
Once the vote has been cast, the voter will get a confirmation reply sms stating the details regarding the vote. Thus this completes the entire workflow of smsdelfva.
We need secure elections, taking everyone into account, and making the whole process a lot easier. All the above get solved by using smsdelfva.
12 Sardar Patel Rd
Anna University
Guindy, Chennai
Tamil Nadu 600025


awk
— Kaushal ModiCollection of awk
examples.
Simple example #
BEGIN { print 42 }
42
The AWK Programming Language #
This section contains awk examples and my notes from the The AWK Programming Language book by Alfred V. Aho, Brian W. Kernighan and Peter J. Weinberger.
An AWK Tutorial #
Beth 4.00 0
Dan 3.75 0
Kathy 4.00 10
Mark 5.00 20
Mary 5.50 22
Susie 4.25 18
Getting Started #
$3 > 0 { print $1, $2 * $3 }
Kathy 40
Mark 100
Mary 121
Susie 76.5
The ,
between $1
and $2
renders as a space in the output by default. That can be changed.
$3 == 0 { print $1 }
Beth
Dan
The Structure of an AWK Program #
Each awk program in this chapter is a sequence of one or more pattern-action statements:
pattern { action }
pattern { action }
...
Either the pattern or the action (but not both) in a pattern-action statement may be omitted. If a pattern has no action as in Code Snippet 4, then each line that the pattern matches is printed.
$3 == 0
Beth 4.00 0
Dan 3.75 0
And if there is an action with no pattern, then the action is performed for every input line.
{ print $1 }
Beth
Dan
Kathy
Mark
Mary
Susie
Simple Output #
Printing Every Line #
{ print }
Beth 4.00 0
Dan 3.75 0
Kathy 4.00 10
Mark 5.00 20
Mary 5.50 22
Susie 4.25 18
{ print $0 }
Beth 4.00 0
Dan 3.75 0
Kathy 4.00 10
Mark 5.00 20
Mary 5.50 22
Susie 4.25 18
Printing Certain Fields #
{ print $1, $3 }
Beth 0
Dan 0
Kathy 10
Mark 20
Mary 22
Susie 18
NF
, the Number of Fields #
{ print NF, $1, $NF }
3 Beth 0
3 Dan 0
3 Kathy 10
3 Mark 20
3 Mary 22
3 Susie 18
Computing and Printing #
{ print $1, $2 * $3 }
Beth 0
Dan 0
Kathy 40
Mark 100
Mary 121
Susie 76.5
Printing Line Numbers #
{ print NR, $0 }
1 Beth 4.00 0
2 Dan 3.75 0
3 Kathy 4.00 10
4 Mark 5.00 20
5 Mary 5.50 22
6 Susie 4.25 18
Putting Text in the Output #
{ print "total pay for", $1, "is", $2 * $3 }
total pay for Beth is 0
total pay for Dan is 0
total pay for Kathy is 40
total pay for Mark is 100
total pay for Mary is 121
total pay for Susie is 76.5
Fancier Output #
- The
print
statement is for quick and easy output. - The
printf
statement is used if you need to format the output exactly the way you want.
Lining Up Fields #
With printf
, no blanks or newlines are produced automatically; you
need to create them yourself. Note the \n
in the printf
statement
in Code Snippet 13.
{ printf("total pay for %s is $%.2f\n", $1, $2 * $3) }
total pay for Beth is $0.00
total pay for Dan is $0.00
total pay for Kathy is $40.00
total pay for Mark is $100.00
total pay for Mary is $121.00
total pay for Susie is $76.50
{ printf("%-8s $%6.2f\n", $1, $2 * $3) }
Beth $ 0.00
Dan $ 0.00
Kathy $ 40.00
Mark $100.00
Mary $121.00
Susie $ 76.50
Selection #
Selection by Comparison #
$2 >= 5
Mark 5.00 20
Mary 5.50 22
Selection by Computation #
$2 * $3 > 50 { printf("$%.2f for %s\n", $2 * $3, $1) }
$100.00 for Mark
$121.00 for Mary
$76.50 for Susie
Selection by Text Content #
$1 == "Susie"
Susie 4.25 18
/y/
Kathy 4.00 10
Mary 5.50 22
It looks like regular expressions cannot be specified in relation to
field variables like $1
, $2
, etc. But I am most likely wrong. For
instance, the /y.*4/
in Code Snippet 19 does a match across fields.
/y.*4/
Kathy 4.00 10
Combinations of Patterns #
Patterns can be combined with parentheses and the logic operators &&
(and), ||
(or) and !
(not).
$2 >= 4 || $3 >= 20
Beth 4.00 0
Kathy 4.00 10
Mark 5.00 20
Mary 5.50 22
Susie 4.25 18
Above, the lines that match both $2>=4
and $3>=20
conditions are
printed just once. But in the case of Code Snippet 21, where multiple
patterns are specified, the program prints a line twice if that line
matches both the conditions.
$2 >= 4
$3 >= 20
Beth 4.00 0
Kathy 4.00 10
Mark 5.00 20
Mark 5.00 20
Mary 5.50 22
Mary 5.50 22
Susie 4.25 18
Code Snippet 22 is a De Morgan’s law variant of Code Snippet 20. Note that the results are the exact same.
!($2 < 4 && $3 < 20)
Beth 4.00 0
Kathy 4.00 10
Mark 5.00 20
Mary 5.50 22
Susie 4.25 18
Data Validation #
When doing data validation, the lines are printed only when they do not match the desirable properties. Think of this use as that of assertions in SystemVerilog. Below, as any of the lines in the example input do not match the failure conditions, there is no output.
NF != 3 { print $0, "number of fields is not equal to 3" }
$2 < 3.35 { print $0, "rate is below minimum wage" }
$2 > 10 { print $0, "rate exceeds $10 per hour" }
$3 < 0 { print $0, "negative hours worked" }
$3 > 60 { print $0, "too many hours worked" }
BEGIN and END #
- The special pattern
BEGIN
matches before the first line of the first input file is read. END
matches after the last line of the last file has been processed.
BEGIN { print "NAME RATE HOURS"; print "" }
{ print }
NAME RATE HOURS
Beth 4.00 0
Dan 3.75 0
Kathy 4.00 10
Mark 5.00 20
Mary 5.50 22
Susie 4.25 18
As noted from Code Snippet 24,
- You can put several statements on a single line if you separate them by semi-colons.
- The
print ""
prints a blank line. - Plain
print
prints the whole line.
Computing with AWK #
Counting #
- The user-created variables are not declared; you just use them.
- The default initial value of variables used as numbers (
awk
auto-detects that) is 0.
$3 > 15 { emp = emp + 1 }
END { print emp, "employees worked more than 15 hours" }
3 employees worked more than 15 hours
Computing Sums and Averages #
END { print NR, "employees" }
6 employees
{ pay = pay + $2 * $3 } # Nothing is printed by this line; only calculation happens
END { print NR, "employees"
print "total pay is", pay
print "average pay is", pay/NR
}
6 employees
total pay is 337.5
average pay is 56.25
Handling Text #
$2 > maxrate { maxrate = $2; maxemp = $1 } # Here maxrate and maxemp variables are updated conditionally; nothing is printed
END { print "highest hourly rate:", maxrate, "for", maxemp }
highest hourly rate: 5.50 for Mary
String Concatenation #
{ names = names $1 " " }
END { print names }
Beth Dan Kathy Mark Mary Susie
Awk automagically figures out that here the names
variable is used to hold string and sets its initial value to a null or empty string.
Printing the Last Input Line #
- Although
NR
retains its values in anEND
action,$0
does not.
So in the below code snippet, we use a user-defined variable last
to store the $0
value of the last line read.
{ last = $0 }
END { print last }
Susie 4.25 18
Built-in Functions #
{ print $1, length($1) }
Beth 4
Dan 3
Kathy 5
Mark 4
Mary 4
Susie 5
Counting Lines, Words and Characters #
{ nc = nc + length($0) + 1 # the trailing "+ 1" is to count the newline character for each line
# $0 does not include the newline
nw = nw + NF
}
END { print NR, "lines", nw, "words", nc, "characters" }
6 lines 18 words 106 characters
Control-Flow Statements #
The control flow statements can be used only in actions.
If-Else Statement #
Code Snippet 33 is similar to Code Snippet 27, but with an if
to protect
against division by zero when computing average.
$2 > 6 { n = n + 1; pay = pay + $2 * $3 }
END { if (n > 0)
printf("%d employees, total pay is %.2f, average pay is %.2f",
n, pay, pay/n) # Note that we can continue a long statement over several lines
# by breaking it after a comma.
else
print "no employees are paid more than $6/hour"
}
no employees are paid more than $6/hour
While Statement #
| 1000 | .06 | 5 |
|---|---|---|
| 1000 | .12 | 7 |
# interest1 - compute compound interest
# input: amount rate years
# output: compounded value at the end of each year
{ i = 1
printf("Amount = %.2f, Rate = %.2f, Years = %.2f\n", $1, $2, $3)
while (i <= $3) {
printf("\tYear %d: %.2f\n", i, $1 * (1 + $2) ^ i)
i = i + 1
}
print ""
}
Amount = 1000.00, Rate = 0.06, Years = 5.00
Year 1: 1060.00
Year 2: 1123.60
Year 3: 1191.02
Year 4: 1262.48
Year 5: 1338.23
Amount = 1000.00, Rate = 0.12, Years = 7.00
Year 1: 1120.00
Year 2: 1254.40
Year 3: 1404.93
Year 4: 1573.52
Year 5: 1762.34
Year 6: 1973.82
Year 7: 2210.68


Data
- Given: $n$ samples of data-tuples, $\{(g_i, y_i, z_i)\}_{i=1}^{n_1}$, $\{(g_i, y_i, z_i)\}_{i=1}^{n_2}$, $n_1 + n_2 = n$
- $g_i$ is a graph $(E, V, w)$ where $w(v_i, v_j) = w_{ij}$ with adjacency-matrix $A^{(i)}$
- a set of effects $y_i$ that we anticipate will be signal
- a set of effects $z_i$ that we anticipate will be noise due to batch effects
Task
- Characterize the composition of signal to batch-effect present
Signal Subgraph Model
- Estimate a probability matrix $P_{y_i}$ for each possible signal class $y_i$
- Determine the edge set $\mathcal{S} = \{(u, v) \in E(G)\}$ that capture the greatest class difference between the $y_i$s
$$L_{A_y}(A, y, \theta) = \prod_{(u, v) \in \mathcal{S}}Bern\left(a_{uv}; p_{uv}\right)\pi_y\prod_{(u, v) \notin \mathcal{S}}Bern\left(a_{uv}; p_{uv}\right)$$
Classification
- Classify depending on the most-likely class based on the likelihood model
- $$\hat{h}(A, \tau_n) = \textrm{argmax}_{y \in Y}\prod_{(u, v) \in \mathcal{S}}Bern\left(a_{uv}; \hat{p}_{uv | y}\right)\hat{\pi}_y$$
Batch Detection
- Pool the data and estimate a ssg based on all of the data, $\mathcal{S}_{global}$
- For each $z_i \in Z$, estimate a local ssg over each $z_i$, $\mathcal{S}_{z_i}$
- Test statistic is $\tau = \frac{1}{|Z|} \sum_{i=1}^{|Z|} jaccard(\mathcal{S}_{z_i}, \mathcal{S}_{global})$
- How similar is the globally estimated ssg to each locally estimated ssg?
Permutation approach for $p$-value
- Pool all of the data, ignoring $z_j$ labels, but retaining $y_i$ labels.
- for each set $z_j \in Z$, randomly select with replacement $n_{y_i}^j$ examples of class $y_i$, where $n_{y_i}^j$ are the number of $y_i$ in the original partition $z_j$.
- Compute the ssg and test statistic appropriately over all $z_j$ for a given permutation iteration.
Does there exist some sort of batch effect in MRI?
Tried with consistent demographics, and non-consistent demographics, as well as with same scanning site repeated multiple times
dMRI, no demographic consideration set
Consider sample with all dMRI studies included
fMRI, no demographic consideration set
consider sample with all fMRI studies included
dMRI, same demographic set
consider dMRI studies with fraction male $.5 \pm .03$ and taken in china (BNU1, BNU3, HNU1, SWU4)
fMRI, same demographic set
consider fMRI studies where fraction male $.5 \pm .03$ and taken in china (BNU1, BNU3, HNU1, SWU4)
dMRI, same site
consider 2 dMRI studies with samples taken at same site (BNU1 and BNU3)
fMRI, same site
consider 2 fMRI studies with samples taken at same site (BNU1 and BNU3)
Next Week
- Consider different possible demographic issues
Cases 1
- Within a study with TRT, partition into 2 sets of sessions, the first session and second session
- Is there a batch effect between sessions of a particular study?
- ie, BNU1, session1 v session 2
Cases 2
- Within a study, partition into 2 sets of subjects, the first half of subjects and second half of subjects
- Is there a batch effect between subjects in a particular study?
- ie, first half of BNU1 vs second half of BNU1.
Cases 3
- Take 2 out-of-sample studies at same site. Sets are study 1 at site $\alpha$, and study 2 at site $\alpha$.
- Is there a batch effect between studies with site held fixed?
- ie, BNU1 and BNU3, both taken at Beijing Normal with same demographics.
Cases 4
- Take 2 studies with same demographics. Sets are study 1 at site $\alpha$ with demographics $d_1$, and study 2 at site $\beta$ with demographics $d_1$.
- Is there a batch effect between sites with demographic held fixed?
- ie, BNU1 and HNU1 both have same demographics (race and age and sex).
Cases 5
- Take 2 studies with not same demographics. Sets are study 1 at site $\alpha$ with demographics $d_1$, and study 2 at site $\beta$ with demographics $d_2$, $d_1 \neq d_2$.
- Is there a batch effect between sites with demographic not the same?
- ie, BNU1 (Beijing Normal University) v. UWM (University of Wisconsin-Madison) taken on different continents, and different races of subjects.
Presentation of Results
- Plot 1: As we vary $|\mathcal{S}|$, what is the impact on 2-fold cross-validated error between set 1 and set 2?
- Plot 2: As we vary $|\mathcal{S}|$, what is the impact on the test statistic $\tau_{obserrved}$?
- Plot 3: As we vary $|\mathcal{S}|$, what is the impact on the $p$-value of the test statistic using the synthetic-permutation approach described?
- How well can we train a logistic regression classifier on the site to identify sex, and how does this compare with chance?


PART II. PROPERTY CONTRACTS, DEBTS, AND LIABILITIES
Note
Part heading added by L 1984, c 79, §1.
Cross References
Community property, see chapter 510.
[§572-21] Presumption of separate property. There is a rebuttable presumption that all property, both real and personal, acquired in the name of the husband or of the wife, without regard to the time of acquisition thereof, is the separate property of the spouse in the name of whom the same has been acquired. [L 1945, c 273, pt of §1; am L 1949, c 242, §3(a); RL 1955, §326-1; HRS §510-1; ren L 1984, c 79, §1]


Cell: 0821-3926-3604
Web: dickysuryo.me
Email<EMAIL_ADDRESS>I am good at creating problems, suggesting high level conspiracies, cursing, spell-casting and pretending to be a genius. I love watching people from a safe distance and sometime I would like to insult their intelligence.
Major: Technical Information


About the Job:
Kenshoo In-Market Data Science team is hiring for the San Francisco office!
The role of the Analytics team is to empower customers to leverage Kenshoo’s suite of algorithmic products such as bid optimizers and to demonstrate and test the value of these algorithmic products internally and externally.
The ideal candidate should have strong data analysis skills as well as programming skills and good understanding of statistics and hypothesis testing.
The ideal candidate will show an intuitive understanding of digital marketing, a strong grasp of business models and statistics, excellent analytical and problem solving skills and the ability to coherently describe complex mathematical concepts to a layman audience.
The candidate should also demonstrate a consultative approach to addressing client needs.
This role will report to Director of Analytics and will be positioned in San Francisco.
Responsibilities:
Requirements
Skills/Attributes Required:
Education/Experience:
Equal Employment Opportunity:
Kenshoo, Inc. is an equal opportunity employer. We strongly encourage and seek applications from women, people of color, and bilingual and bicultural individuals, as well as members of the lesbian, gay, bisexual, and transgender communities. Applicants shall not be discriminated against because of race, religion, sex, national origin, ethnicity, age, disability, political affiliation, sexual orientation, gender identity, color, marital status, or medical condition including acquired immune deficiency syndrome (AIDS) and AIDS-related conditions. Also pursuant to the San Francisco Fair Chance Ordinance, we encourage and will consider for employment qualified applicants with arrest and conviction records.
Applicants with Disabilities:
Reasonable accommodation will be made so that qualified disabled applicants may participate in the application process. Please advise in writing of special needs at the time of application.


IFBluetoothManagerDelegate Protocol Reference
| Conforms to | NSObject |
|---|---|
| Declared in | IFBluetoothManager.h |
Overview
Managers are the most important part of the application, you can get all the information about position, bluetooth data and content throught it’s delegates.
– manager:didUpdateTransmitter:
Transmitter RSSI updated
- (void)manager:(IFBluetoothManager *)manager didUpdateTransmitter:(IFTransmitter *)transmitter
Parameters
manager |
IFBluetoothManager manager |
|---|---|
transmitter |
Transmitter object |
Discussion
Transmitter RSSI updated
Declared In
IFBluetoothManager.h
– manager:didDiscoverActiveBeaconsForFloorplan:
We found some active beacons for the floor
- (void)manager:(IFBluetoothManager *)manager didDiscoverActiveBeaconsForFloorplan:(IFMFloorplan *)floorplan
Parameters
manager |
IFBluetoothManager manager |
|---|---|
floorplan |
Floorplan with available beacons |
Discussion
We found some active beacons for the floor
Declared In
IFBluetoothManager.h
– manager:didDiscoverActiveBeaconsForVenue:floorplan:
We found some active beacons for the venue and floor
- (void)manager:(IFBluetoothManager *)manager didDiscoverActiveBeaconsForVenue:(IFMVenue *)venue floorplan:(IFMFloorplan *)floorplan
Parameters
manager |
IFBluetoothManager manager |
|---|---|
venue |
IFMVenue with available beacons |
floorplan |
Floorplan with available beacons |
Discussion
We found some active beacons for the venue and floor
Declared In
IFBluetoothManager.h
– manager:didLostAllBeaconsForFloorplan:
We lost the connection with all of the beacons inside the floor
- (void)manager:(IFBluetoothManager *)manager didLostAllBeaconsForFloorplan:(IFMFloorplan *)floorplan
Parameters
manager |
IFBluetoothManager manager |
|---|---|
floorplan |
Floorplan with no active beacons |
Discussion
We lost the connection with all of the beacons inside the floor
Declared In
IFBluetoothManager.h
– manager:didLostAllBeaconsForVenue:
We lost the connection with all of the beacons inside the venue
- (void)manager:(IFBluetoothManager *)manager didLostAllBeaconsForVenue:(IFMVenue *)venue
Parameters
manager |
IFBluetoothManager manager |
|---|---|
venue |
IFMVenue with no active beacons |
Discussion
We lost the connection with all of the beacons inside the venue
Declared In
IFBluetoothManager.h
– manager:didChangeTransmittersCount:
There are some new beacons nearby
- (void)manager:(IFBluetoothManager *)manager didChangeTransmittersCount:(NSDictionary *)transmitters
Parameters
manager |
IFBluetoothManager manager |
|---|---|
transmitters |
Nearby beacons array |
Discussion
There are some new beacons nearby
Declared In
IFBluetoothManager.h
– manager:didDiscoverTransmitter:
A new beacon has been found
- (void)manager:(IFBluetoothManager *)manager didDiscoverTransmitter:(IFTransmitter *)transmitter
Parameters
manager |
IFBluetoothManager manager |
|---|---|
transmitter |
A Beacon object |
Discussion
A new beacon has been found
Declared In
IFBluetoothManager.h
– manager:didLostTransmitter:
A Beacon dissapeared
- (void)manager:(IFBluetoothManager *)manager didLostTransmitter:(IFTransmitter *)transmitter
Parameters
manager |
IFBluetoothManager manager |
|---|---|
transmitter |
A Beacon object |
Discussion
A Beacon dissapeared
Declared In
IFBluetoothManager.h
– manager:isValidPeripheral:advertisementData:RSSI:
This method allow to ignore some peripherals. If method is implemented and returns NO, then peripheral is ignored and not process further.
- (BOOL)manager:(IFBluetoothManager *)manager isValidPeripheral:(CBPeripheral *)peripheral advertisementData:(NSDictionary *)advertisementData RSSI:(NSNumber *)RSSI
Parameters
manager |
bluetooth manager |
|---|---|
peripheral |
peripheral to check |
advertisementData |
advertisement data recived in peripheral packet |
RSSI |
peripheral RSSI value |
Return Value
If NO peripheral is ignored and not processed. If YES, peripheral is processed further and at some point pass to IFTransmitter.
Discussion
This method allow to ignore some peripherals. If method is implemented and returns NO, then peripheral is ignored and not process further.
Declared In
IFBluetoothManager.h
– manager:didUpdateCentralManagerState:
Invoked whenever the central manager’s state has been updated.
- (void)manager:(IFBluetoothManager *)manager didUpdateCentralManagerState:(CBCentralManagerState)centralManagerState
Parameters
manager |
bluetooth manager |
|---|---|
central |
current state of the CBCentralManger |
Discussion
Invoked whenever the central manager’s state has been updated.
Declared In
IFBluetoothManager.h


A former Chelsea soccer player tried to substitute himself off after forgetting his own shirt number
by Barnaby Lane- AS Monaco’s Tiemoue Bakayoko inadvertently tried to substitute himself off the field on Saturday after forgetting his shirt number.
- The 25-year-old is in his second spell with Monaco, this time as number six after being 14 previously.
- When number 14 was called at the weekend, Bakayoko wandered over to the sidelines, clapped the home fans, and tried to exit the field.
- Only when manager Leonardo Jardim pointed out he now wears number six, did the former Chelsea FC midfielder realise his error.
- Read more of our soccer coverage here.
- Visit Business Insider’s homepage for more stories.
Tiemoue Bakayoko had a difficult time in England with Chelsea FC.
And his struggles have continued with AS Monaco, so much so that he inadvertently tried to substitute himself off the field during his side’s win over Amiens on Saturday.
Bakayoko is in his second spell with Monaco after having returned to Stade Louis II on-loan from Chelsea during the summer.
Since returning, however, the 25-year-old has swapped shirt numbers. He used to wear number 14, but now he wears number six.
At the weekend, that fact slipped Bakayoko’s mind.
Deep into the second half, the substitute board was raised signalling for Monaco’s number 14 to come off and be replaced by Jean-Kevin Augustin.
Forgetting he no longer wears number 14, Bakayoko wandered over to the sidelines, clapped the home fans, and tried to exit the field.
Only when manager Leonardo Jardim informed him he actually now wears number six on his shirt, the midfielder realised his mistake, creasing over in a fit of laughter.
Augustin, Jardim, and even the fourth official also saw the funny side – all three chuckling at the former Chelsea man’s lack of memory.
After the match, the club posted the video on their Twitter page, to which Bakayoko commented: “Give me back my 14 please.”
Monaco next takes on Angers SCO on Saturday, December 14.
Read more:


Match Play. Red trails 4-6 to 7. How should Red play 63?
pips Red=132 White=65
(CorrectAnswer= 0/0, ErrorScore= 0)
Red’s game plan is to make the 8-pt and complete a full prime. Note that 7/1*, 4/1 is completely wrong giving White chances he doesn’t deserve, and that move is a quintuple blunder.
The question is do you slot the 8-pt with 20/17, 14/8 or bring a builder with 20/11? The answer is that not leaving White an immediate 61 joker is best but it is very close.
Note that 22/13 is also badly wrong. With a 58-pip deficit in the race Red should hold on to the 22-pt while the matter of his 8-pt is resolved.
This is a question of priorities Red’s 8-pt is the key to this position so that is where the attention needs to be focused.
The question is do you slot the 8-pt with 20/17, 14/8 or bring a builder with 20/11? The answer is that not leaving White an immediate 61 joker is best but it is very close.
Note that 22/13 is also badly wrong. With a 58-pip deficit in the race Red should hold on to the 22-pt while the matter of his 8-pt is resolved.
This is a question of priorities Red’s 8-pt is the key to this position so that is where the attention needs to be focused.
1. Cubeful 2-ply 20/11 Eq.: +1.167 0.867 0.217 0.005 - 0.133 0.034 0.000 2. Cubeful 2-ply 20/17 14/8 Eq.: +1.154 (-0.013) 0.862 0.215 0.005 - 0.138 0.041 0.000 3. Cubeful 2-ply 14/5 Eq.: +1.123 (-0.044) 0.847 0.214 0.006 - 0.153 0.035 0.000 4. Cubeful 2-ply 22/13 Eq.: +1.038 (-0.129) 0.850 0.169 0.004 - 0.150 0.045 0.000 5. Cubeful 2-ply 22/16 14/11 Eq.: +1.012 (-0.155) 0.839 0.167 0.004 - 0.161 0.047 0.000


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