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History of Slovakia

Bronze Age

Iron Age and the Roman era

The Slavs

The empire of King Samo

The remnants of the Slavic population settled in the Middle Danube and they were unified by King Samo, after a successful Slavic insurrection against the Avar Khaganate in 623. In 631, Samo defeated the Frankish army of King Dagobert I at the Battle of Wogastisburg. Sam's Empire, the first known political formation of Slavs, disappeared after the death of its founder in 665.

The era of Great Moravia

The first recorded mention of Slavic princes near Pannonia goes back to 803 CE. In 805, the presence of Prince Vratislav, Lord of Bratislava Castle, signifies the arrival of the second historic Slav in the Middle Danube. The important moment in history was when Cyril and Methodius came to Great Moravia. They further developed writing in the Slav language.

Important events during the period of the Kingdom of Svätopluk:

•874 Occupation of the Vistule Basin by Svätopluk
•882 Occupation of the territory of Bohemia (present-day Czech Republic, excluding Moravia)
•883 Invasion and Occupation of Pannonia
•889 Occupation of the territory of the Lusatian Serbs
•890 Treaty with Arnoul, confirmation of the sovereignty of Svätopluk I in Bohemia
•892 Driving back of the common attack by the Frankish, Alaman and Bavarian armies and the Magyar tribes
•893 Driving back of the attack by the armies of Arnoul, and upon his request, the signing of a peace treaty with Svätopluk
•894 Death of King Svätopluk I, peace for the Slavs with the Bavarians, first invasion by the Magyars into the Transdanubian region.

Kingdom of Hungary

From the beginning of the 10th century, the Ugrian tribes of the Magyars (Hungarians), progressively imposed their authority on the Slav tribes. At the same time, they began to adopt the lifestyle of the Slavs and Germans, who had been already practicing Christianity and had lived a settled life. During this transitional period, the Magyars frequently conducted raids to surrounding territories. Such activities continued until the Battle of Augsburg on the Lech River in 955, when Otto, King of the Germans disastrously destroyed the troops of the Horka. As a direct result the Árpád\'s tribe started to impose their authority over other Magyar tribes in Transdanubia. The territory of the present-day Slovakia became progressively integrated into the developing Principality of Hungary in the early 10th century. In 997 the head of the old Magyars, Geza, died, and the question of his succession arose. When the first ruling dynasty of the Kingdom, the Árpáds died out (1301), struggle broke out for the succession of the Hungarian crown. During this anarchic period, the central power weakened and various local oligarchies ruled the lands. From the late 1290s, Matthew Csák the self-titled \"Lord of the Váh and the Tatras\" was the de facto ruler of much of the territories of present-day Slovakia, from his seat at Trenčín. After his defeat at the Battle of Rozhanovce in 1312, by the newly elected Hungarian king Charles Robert from the Angevine dynasty, Csák\'s influence started to decline. He died in 1321 in Trenčín.

World War II

Czechoslovakia after World War II

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I think even if we manage to make the best of the situation, weld still be economically worse off, because of all the harbour cities which will be reclaimed by the sea and all the lost land mass


 Getting Started with Neural Networks for Algorithmic Trading - Robot Wealth

Getting Started with Neural Networks for Algorithmic Trading

Posted on Sep 06, 2017 by Kris Longmore

This article is adapted from one of the units of Advanced Algorithmic Trading. If you like what you see, check out the entire curriculum here. Find out what Robot Wealth is all about here.

If you’re interested in using artificial neural networks (ANNs) for algorithmic trading, but don’t know where to start, then this article is for you. Normally if you want to learn about neural networks, you need to be reasonably well versed in matrix and vector operations – the world of linear algebra. This article is different. I’ve attempted to provide a starting point that doesn’t involve any linear algebra and have deliberately left out all references to vectors and matrices. If you’re not strong on linear algebra, but are curious about neural networks, then I think you’ll enjoy this introduction. In addition, if you decide to take your study of neural networks further, when you do inevitably start using linear algebra, it will probably make a lot more sense as you’ll have something of head start.
The best place to start learning about neural networks is the perceptron. The perceptron is the simplest possible artificial neural network, consisting of just a single neuron and capable of learning a certain class of binary classification problems.1 Perceptrons are the perfect introduction to ANNs and if you can understand how they work, the leap to more complex networks and their attendant issues will not be nearly as far. So we will explore their history, what they do, how they learn, where they fail. We’ll build our own perceptron from scratch and train it to perform different classification tasks which will provide insight into where they can perform well, and where they are hopelessly outgunned. Lastly, we’ll explore one way we might apply a perceptron in a trading system.2

A Brief History of the Perceptron

The perceptron has a long history, dating back to at least the mid 1950s. Following its discovery, the New York Times ran an article that claimed that the perceptron was the basis of an artificial intelligence (AI) that would be able to walk, talk, see and even demonstrate consciousness. Soon after, this was proven to be hyperbole on a staggering scale, when the perceptron was shown to be wholly incapable of classifying certain types of problems. The disillusionment that followed essentially led to the first AI winter, and since then we have seen a repeating pattern of hyperbole followed by disappointment in relation to artificial intelligence.3
Still, the perceptron remains a useful tool for some classification problems and is the perfect place to start if you’re interested in learning more about neural networks. Before we demonstrate it in a trading application, let’s find out a little more about it.

Artificial Neural Networks: Modelling Nature

Algorithms modelled on biology are a fascinating area of computer science. Undoubtedly you’ve heard of the genetic algorithm, which is a powerful optimization tool modelled on evolutionary processes. Nature has been used as a model for other optimization algorithms, as well as the basis for various design innovations. In this same vein, ANNs attempt to learn relationships and patterns using a somewhat loose model of neurons in the brain. The perceptron is a model of a single neuron.4
In an ANN, neurons receive a number of inputs, weight each of those inputs, sum the weights, and then transform that sum using a special function called an activation function, of which there are many possible types. The output of that activation function is then either used as the prediction (in a single neuron model) or is combined with the outputs of other neurons for further use in more complex models, which we’ll get to in another article.
Here’s a sketch of that process in an ANN consisting of a single neuron:

Here, \(x_1, x_2, etc\) are the inputs. \(b\) is called the bias term, think of it like the intercept term in a linear model \(y = mx + b\). \(w_1, w_2, etc\) are the weights applied to each input. The neuron firstly sums the weighted inputs (and the bias term), represented by \(S\) in the sketch above. Then, \(S\) is passed to the activation function, which simply transforms \(S\) in some way. The output of the activation function, \(z\) is then the output of the neuron.
The idea behind ANNs is that by selecting good values for the weight parameters (and the bias), the ANN can model the relationships between the inputs and some target. In the sketch above, \(z\) is the ANN’s prediction of the target given the input variables.
In the sketch, we have a single neuron with four weights and a bias parameter to learn. It isn’t uncommon for modern neural networks to consist of hundreds of neurons across multiple layers, where the output of each neuron in one layer is input to all the neurons in the next layer. Such a fully connected network architecture can easily result in many thousands of weight parameters. This enables ANNs to approximate any arbitrary function, linear or nonlinear.
The perceptron consists of just a single neuron, like in our sketch above. This greatly simplifies the problem of learning the best weights, but it also has implications for the class of problems that a perceptron can solve.

What’s an Activation Function?

The purpose of the activation function is to take the input signal (that’s the weighted sum of the inputs and the bias) and turn it into an output signal. There are many different activation functions that convert an input signal in a slightly different way, depending on the purpose of the neuron.
Recall that the perceptron is a binary classifier. That is, it predicts either one or zero, on or off, up or down, etc. It follows then that our activation function needs to convert the input signal (which can be any real-valued number) into either a one or a zero5corresponding to the predicted class.
In biological terms, think of this activation function as firing (activating) the neuron (telling it to pass the signal on to the next neuron) when it returns 1, and doing nothing when it returns 0.
What sort of function accomplishes this? It’s called a step function, and its mathematical expression looks like this:
\[f(z) =\begin{cases} 1, & \text{if $z$ > 0} \\ 0, & \text{otherwise} \end{cases}\]
And when plotted, it looks like this:

This function then transforms any weighted sum of the inputs (S) and converts it into a binary output (either 1 or 0). The trick to making this useful is finding (learning) a set of weights, \(w\), that lead to good predictions using this activation function.

How Does a Perceptron Learn?

We already know that the inputs to a neuron get multiplied by some weight value particular to each individual input. The sum of these weighted inputs is then transformed into an output via an activation function. In order to find the best values for our weights, we start by assigning them random values and then start feeding observations from our training data to the perceptron, one by one. Each output of the perceptron is compared with the actual target value for that observation, and, if the prediction was incorrect, the weights adjusted so that the prediction would have been closer to the actual target. This is repeated until the weights converge.
In perceptron learning, the weight update function is simple: when a target is misclassified, we simply take the sign of the error and then add or subtract the inputs that led to the misclassifiction to the existing weights.
If that target was -1 and we predicted 1, the error is \(-1 -1 = -2\). We would then subtract each input value from the current weights (that is, \(w_i = w_i – x_i\)). If the target was 1 and we predicted -1, the error is \(1 – -1 = 2\), so then add the inputs to the current weights (that is, \(w_i = w_i + x_i\)).6
This has the effect of moving the classifier’s decision boundary (which we will see below) in the direction that would have helped it classify the last observation correctly. In this way, weights are gradually updated until they converge. Sometimes (in fact, often) we’ll need to iterate through each of our training observations more than once in order to get the weights to converge. Each sweep through the training data is called an epoch.

Implementing a Perceptron from Scratch

Next, we’ll code our own perceptron learning algorithm from scratch using R. We’ll train it to classify a subset of the iris data set.7
In the full iris data set, there are three species. However, perceptrons are for binary classification (that is, for distinguishing between two possible outcomes). Therefore, for the purpose of this exercise, we remove all observations of one of the species (here, virginica), and train a perceptron to distinguish between the remaining two. We also need to convert the species classification into a binary variable: here we use 1 for the first species, and -1 for the other. Further, there are four variables in addition to the species classification: petal length, petal width, sepal length and sepal width.  For the purposes of illustration, we’ll train our perceptron using only petal length and width and drop the other two measurements. These data transformations result in the following plot of the remaining two species in the two-dimensional feature space of petal length and petal width:

The plot suggests that petal length and petal width are strong predictors of species – at least in our training data set. Can a perceptron learn to tell them apart?
Training our perceptron is simply a matter of initializing the weights (here we initialize them to zero) and then implementing the perceptron learning rule, which just updates the weights based on the error of each observation with the current weights. We do that in a for()  loop which iterates over each observation, making a prediction based on the values of petal length and petal width of each observation, calculating the error of that prediction and then updating the weights accordingly.
In this example we perform five sweeps through the entire data set, that is, we train the perceptron for five epochs. At the end of each epoch, we calculate the total number of misclassified training observations, which we hope will decrease as training progresses. Here’s the code:

Here’s the plot of the error rate:

We can see that it took two epochs to train the perceptron to correctly classify the entire dataset. After the first epoch, the weights hadn’t been sufficiently updated. In fact, after epoch 1, the perceptron predicted the same class for every observation! Therefore it misclassified 50 out of the 100 observations (there are 50 observations of each species in the data set). However after two epochs, the perceptron was able to correctly classify the entire data set by learning appropriate weights.
Another, perhaps more intuitive way, to view the weights that the perceptron learns is in terms of its decision boundary. In geometric terms, for the two-dimensional feature space in this example, the decision boundary is the a straight line separating the perceptron’s predictions. On one side of the line, the perceptron always predicts -1, and on the other, it always predicts 1.8
We can derive the decision boundary from the perceptron’s activation function:
where \(z = w_1x_1 + w_2x_2 + b\)
The decision boundary is simply the line that defines the location of the step in the activation function. That step occurs at \(z=0\), so our decision boundary is given by
\[w_1x_1 + w_2x_2 + b\ = 0 \]
Equivalently \[x_2 = -\frac{w_1}{w_2}x_1 – \frac{b}{w_2}\]
which defines a straight line in \(x_1, x_2\) feature space.
In our iris example, the perceptron learned the following decision boundary:

Here’s the complete code for training this perceptron and producing the plots shown above:

Congratulations! You just built and trained your first neural network.
Let’s now ask our perceptron to learn a slightly more difficult problem. Using the same iris data set, this time we remove the setosa species and train a perceptron to classify virginica and versicolor on the basis of their petal lengths and petal widths. When we plot these species in their feature space, we get this:

This looks a slightly more difficult problem, as this time the difference between the two classifications is not as clear cut. Let’s see how our perceptron performs on this data set.
This time, we introduce the concept of the learning rate, which is important to understand if you decide to pursue neural networks beyond the perceptron. The learning rate controls the speed with which weights are adjusted during training. We simply scale the adjustment by the learning rate: a high learning rate means that weights are subject to bigger adjustments. Sometimes this is a good thing, for example when the weights are far from their optimal values. But sometimes this can cause the weights to oscillate back and forth between two high-error states without ever finding a better solution. In that case, a smaller learning rate is desirable, which can be thought of as fine tuning of the weights.
Finding the best learning rate is largely a trial and error process, but a useful approach is to reduce the learning rate as training proceeds. In the example below, we do that by scaling the learning rate by the inverse of the epoch number.
Here’s a plot of our error rate after training in this manner for 400 epochs:

You can see that training proceeds much less smoothly and takes a lot longer than last time, which is a consequence of the classification problem being more difficult. Also note that the error rate is never reduced to zero, that is, the perceptron is never able to perfectly classify this data set. Here’s a plot of the decision boundary, which demonstrates where the perceptron makes the wrong predictions:

Here’s the code for this perceptron:

Where Do Perceptrons Fail?

In the first example above, we saw that our versicolor and setosa iris species could be perfectly separated by a straight line (the decision boundary) in their feature space. Such a classification problem is said to be linearly separable and (spoiler alert) is where perceptrons excel. In the second example, we saw that versicolor and virginica were almost linearly separable, and our perceptron did a reasonable job, but could never perfectly classify the whole data set. In this next example, we’ll see how they perform on a problem that isn’t linearly separable at all.
Using the same iris data set, this time we classify our iris species as either versicolor or other (that is setosa and virginica get the same classification) on the basis of their petal lengths and petal widths. When we plot these species in their feature space, we get this:

This time, there is no straight line that can perfectly separate the two species. Let’s see how our perceptron performs now. Here’s the error rate over 400 epochs and the decision boundary:

We can see that the perceptron fails to distinguish between the two classes. This is typical of the performance of the perceptron on any problem that isn’t linearly separable. Hence my comment at the start of this unit (see footnote 2) that I’m skeptical that perceptrons can find practical application in trading. Maybe you can find a use case in trading, but even if not, they provide an excellent foundation for exploring more complex networks which can model more complex relationships.

A Perceptron Implementation for Algorithmic Trading

The Zorro trading automation platform includes a flexible perceptron implementation. If you haven’t heard of Zorro, it is a fast, accurate and powerful backtesting/execution platform that abstracts a lot of tedious programming tasks so that the user is empowered to concentrate on efficient research. It uses a simple C-based scripting language that takes almost no time to learn if you already know C, and a week or two if you don’t (although of course mastery can take much longer). This makes it an excellent choice for independent traders and those getting started with algorithmic trading. While the software sacrifices little for the abstraction that enables efficient research, experienced quant developers or those with an abundance of spare time might take issue with that aspect of the software, as it’s not open source, so it isn’t for everyone. But it’s a great choice for beginners and DIY traders who maintain a day job. If you want to learn to use Zorro, even if you’re not a programmer, we can help.
Zorro’s perceptron implementation allows us to define any features we think are pertinent, and to specify any target we like, which Zorro automatically converts it to a binary variable (by default, positive values are given one class; negative values the other). After training, Zorro’s perceptron predicts either a positive or negative value corresponding to the positive and negative classes respectively.
Here’s the Zorro code for implementing a perceptron that tries to predict whether the 5-day price change in the EUR/USD exchange rate will be greater than 200 pips, based on recent returns and volatility, whose predictions are tested under a walk-forward framework:

Zorro firstly outputs a trained perceptron for predicting long and short 5-day price moves greater than 200 pips for each walk-forward period, and then tests their out-of-sample predictions.
Here’s the walk-forward equity curve of our example perceptron trading strategy:

I find this result particularly interesting because I expected the perceptron to perform poorly on market data, which I find it hard to imagine falling into the linearly separable category. However, sometimes simplicity is not a bad thing, it seems.

I hope this article not only whet your appetite for further exploration of neural networks, but facilitated your understanding of the basic concepts, without getting too hung up on the math.
I intended for this article to be an introduction to neural networks where the perceptron was to be nothing more than a learning aid. However, given the surprising walk-forward result from our simple trading model, I’m now going to experiment with this approach a little further. If this interests you too, some ideas you might consider include extending the backtest, experimenting with different signals and targets, testing the algorithm on other markets and of course considering data mining bias. I’d love to hear about your results in the comments.
Thanks for reading!

(7) Comments

[…] Getting Started with Neural Networks for Algorithmic Trading [Robot Wealth] […]

September 8, 2017 at 11:03 am

The line :
might be in the wrong place.
I had to move it then the script worked.

September 18, 2017 at 9:32 pm

Thanks Jason!

February 13, 2018 at 10:17 pm

Zorro fails to compile the script if these lines are present in this shape:
var l = adviseLong(PERCEPTRON+BALANCED, ObjLong, Sig1, Sig2, Sig3, Sig4);
var s = adviseShort(PERCEPTRON+BALANCED, ObjShort, Sig1, Sig2, Sig3, Sig4);
They seem to be fine with the function syntax yet it doesn’t like it throwing series of:
Error 062: Can’t open test_nn_from_web_24.c [t:]
Commenting the adviseLong and adviseShort helps but then it doesn’t make any sense.

February 14, 2018 at 2:31 pm

Error 062 occurs when Zorro can’t find a file it needs. It could be that you didn’t train your perceptrons before trying to run a backtest? During the train process, Zorro outputs the perceptron as a .c file, which is of course required before running a simulation. If that’s the case, simply hit the [Train] button on the Zorro GUI, wait for training to finish, then hit [Test].
This error can also arise when Zorro doesn’t have the correct permissions to access the files it needs. In that case, try running Zorro with Admin privileges.

February 17, 2018 at 5:16 am

” It could be that you didn’t train your perceptrons before trying to run a backtest?”
Agrh stupid me I didn’t press the button… it’s working now 😉
However it looks good only till the end of Jan 2015 – by the end the next year it lost everything.
BTW: ATR it very sensitive to the starting point of the time series: bars as old as 2000 ago can affect today results. It’s enough to shorten or lengthen the series to get different atr(10) at the end. What daily volatility almost six years ago has to today volatility? Yet it affects the atr outcome. So the dataset starting point may affect the results – did you try to move it backward or forward by a year or two?

August 26, 2019 at 7:48 am

Hello. Can we be in touch to learn more about the module

Leave a Comment


The Water Mesada

(Continued from Part 1) On the first day, we went into the ceremonial tent and lined up by the Mesa -- a table filled with ancient artifacts from the Chavin empire -- a pre Mayan civilization in Peru that used Huachuma as a sacred sacrament. Don Howard had explained the purpose of the Mesa and it’s importance in ceremony with Huachuma, but it was a somewhat daunting sight and so far removed from any concept of spirituality that I had ever experienced that I felt a little nervous and unsure of what I was getting myself into...


Chatbots in Africa

How chatbots solve day-to-day problems in Africa

Watch it in action!

1. Technology in Africa Technology in Africa

Annual mobile phone shipments in Africa
Internet and mobile penetration

in 2011 only 7% of the purchased phones were smart, in 2015 that number was 58%. So we think it's time to switch to smarter solutions.

Most people in Africa use text messages to send money with a service called M‑Pesa

And there are even more startups tackling different problems, but they are not yet utilizing the growing smartphone accessibility.

2. Existing Solutions Existing Solutions

Already existing local startups

After social network services dealing with education and healthcare are the most desired. Agricultural information are also on top of the list. For example: farmers always check current market prices before selling their products.

Targeting agriculture and finance

Esoko, WeFarm, M-Pesa and many other local startups all target specific problems mostly present in the mentioned branches using text messages or USSD services.

Using text-message services

When using text-based services people have to remember codes if they want to retrieve information, with USSD services they are given questions and they answer them.

Text-based services work like chatbots do.

Sounds familiar?

Text-based services work like chatbots do.

A conversational interface

When interacting with a chatbot, users see a conversational interface which is not new to them since Africa has 5.3 million Facebook users and 78% of smartphone owners use their device for social networking and chatting.

Faster than asking people

Most people don't really see Google as a way of problem solving: they'd rather ask a friend. So with a chatbot they access the information with the same method, but much faster.

Trustworthy Sources

While a friend is not the most reliable source of information, a chatbot can get accurate information in a blink of an eye.

We created Nuru a bot tailored for the needs of people in Kenya and Ghana.

01Give your basic information

New users are walked through the onboarding process. They answer questions to fill up their profile all while getting familiar with the app's interface

Nuru onboarding - Give your basic information

02Manage your finances

After setting up the account users can instantly send and recieve money, or pay for goods they purchased through our chatbot. So all their financing needs are covered, of course only after a safe authentication process

Nuru onboarding - Manage your finances

03Browse through job listings

Searching for seasonal jobs has never been easier. If the user asks for job suggestions, Nuru will list all the opportunities nearby. Further information on the job can be acquired via text or a phone call.

Nuru onboarding - Browse through job listings

04Get fair price suggestions

Farmers, for example, can post ads and find buyers for their products by simply answering some questions. Nuru can calculate current market prices and give an estimate for the given amount.

Nuru onboarding - Get fair price suggestions

05Live a healthier life

Empathy is key when it comes to chatbots. Nuru can can give daily tips on how to live a healthier life or help during the course of the pregnancy, either with wheather it is daily notifications or suggesting doctors nearby.

Nuru onboarding - Live a healthier life

3. Designing a conversational interface Designing a conversational interface

Designing a conversational interface flowchart

4. Branding Nuru chatbot branding

Nuru chatbot branding inspiration

We found inspiration in the traditional african Andinkra symbols. We wanted to create a sign which also reminds us of these ancient symbols.

We found inspiration in the traditional african Andinkra symbols

Logo design

After experimenting with different variations, we found the best one which communicated our main goal the most - giving the opportunity for the people to connect.

Nuru logo sketch


Are Directional Tires Worth the Cost?

When you’re looking into the best ways to improve the performance of your vehicle, whether for recreational or weather reasons, chances are you’ve come across recommendations for directional tires. If you’re thinking about the possibility of purchasing directional tires, the first thing you’re likely to notice is the cost of this type of tire is higher than other options. But why is there such a large price difference? Instead of letting the price tag stop you from at least considering the benefits of installing directional tires on your car, let’s look into the science and logic behind the design and prices. The experts at Wrecktify Collision & Repair want you to understand and be able to fully assess the options at hand in order to make the best decision for your driving needs.

Directional tires possess a variety of benefits and purposes, and in order to decide whether or not to utilize them, we want you to know all about the benefits they provide.

Why So Expensive?

The key “con” of directional tires is, of course, the elevated price. But what makes these tires more expensive than the typically equipped symmetric or asymmetric tires? The answer is surprisingly straightforward: the rubber compounds that allow the tires to reach a higher performance level and grip the road better is what boosts the price tag. Having this specific tire tread allows for enhanced driving experience and general functional improvements. Simply put, directional tires are more expensive because they’re just better!

Safetydirectional tires

Directional tires are made to be ideal for all types of weather because they are designed specifically for the purpose of performance, regardless of any snow or rainwater on the road. When the roads are wet, the V-shaped pattern of the treads pushes the water to the outsides of the tires. This enables your vehicle to have consistent traction on the pavement and reduces the risk of you slipping or hydroplaning. Whether the weather calls for snow, rain, or sunshine, directional tires will ensure you and your family will remain safe when you’re on the road. It’s a significant plus, and it’s a highly recommended investment to make for families living in areas with heavy and frequent rainfall.

Even if you aren’t going to be dealing with a large amount of rain and snow, directional tires are the best for optimal handling on dry pavement. These are the tires you’ll find on race cars and professional drivers’ cars, so you can expect to have a professional-grade driving experience every time you hit the blacktop. And directional tires can do much more than making any car just a little more fun to drive. When you’re driving at a higher speed, you can rely on your tires to maintain control and stability on the road, again ensuring that you remain safe in your car. How your car drives and handles may not be the first thing on everyone’s mind, but once you feel the difference a good set of tires makes, you’ll never want to go back.

Fuel Efficiency

Because directional tires have better traction and handling, they will increase your car’s fuel efficiency. Of course, your driving patterns and acceleration habits will affect your gas mileage regardless of the type of tires. However, directional tires have less “rolling resistance” because of their design, and therefore require less force to accelerate out of a turn or at a traffic light. It may not seem like much, but the smaller amount of force makes a huge difference when you’re looking at the big picture. Tires that provide a lower rolling resistance are estimated to save one to four percent per gallon of fuel compared to the average asymmetric or symmetric tires. So while there may be a higher price of the initial investment when it comes to directional tires, you will most likely save money to make up for that cost in the long run.

Any Downfalls?

Aside from the higher buying price, the only negatives when it comes to directional tires are the smaller lifespan and potentially more expensive maintenance. As you probably already know, the longer you drive on a set of tires, the more the treads wear down, and usually specific areas of the tires are broken down more due to the turning and shifting weight of your vehicle. Normally, to extend the life of your tires instead of just switching them out and buying new ones, you can go in to get your tires rotated. With symmetric and even asymmetric tires, you can rotate them from front to back and side to side. However, thanks to directional tires being… well… directional, they can usually only be rotated on the front-to-back plane, making the rate of replacement higher than alternative treads.

That’s not to say it’s absolutely impossible to rotate directional tires from one side to the other. Most vehicle repair or tire shops are able to perform the task, but the job requires quite a few more steps and more equipment. Side-to-side rotation requires the dismounting of the tires, flipping them, and then remounting them. It may not seem like much, but it’s a tedious and time-consuming task, and you will have to pay extra to have it done.

Though the price tag on these tires is oftentimes daunting to potential buyers in comparison to the standard symmetric or asymmetric tire patterns, most experts would say directional tires are worth the extra investment. For the safety of you and your family and overall superior driving experience, directional tires will live up to the hype. However, because of the extra costs and hassle, not everyone may be able or willing to put in the extra funds. In order to find the best decision for you, your driving habits, and your budget, talk to a local repair shop to get professional advice. Our technicians at Wrecktify would be more than happy to share their expertise and experience with you, so don’t hesitate to call!

Contact Us


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Page "Political spectrum" ¶ 69
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Inglehart's and like

Inglehart's and .
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See for Inglehart's national chart.

two-factor and form
The two-factor Hull White model contains an additional disturbance term whose mean reverts to zero, and is of the form:
TANs are believed to provide additional security because they act as a form of two-factor authentication.

two-factor and original
In 1968 Herzberg stated that his two-factor theory study had already been replicated 16 times in a wide variety of populations including some in Communist countries, and corroborated with studies using different procedures that agreed with his original findings regarding intrinsic employee motivation making it one of the most widely replicated studies on job attitudes.

two-factor and ;
Most of the published literature uses a simplified two-factor definition ; i. e., dermatillomania is 1 ) repetitive picking at the skin that 2 ) results in significant distress or social or occupational impairment.

two-factor and them
The Russian player starts the game with a three-factor worker unit in Moscow, and two-factor worker units in Leningrad, Kiev, Kharkov and Stalino ( the latter three will probably be lost to German attacks ), but receives nine more worker units ( all but one of them single-factor ) over the first year or so of the war.

two-factor and
GoldKey Tokens with Flash combine the same two-factor authentication features of GoldKey with up to 64GB of built-in encrypted flash storage all in the same tiny package.
Schachter & Singer ( 1962 ) The two-factor theory of emotion, or Schachter Singer theory, states that emotion is a function of both cognitive factors and physiological arousal.

two-factor and ",
Soliciting multiple answers to challenge questions may be considered strong authentication but, unless the process also retrieves " something the user has " or " something the user is ", it would not be considered two-factor authentication.

two-factor and which
A simple setting in which interactions can arise is a two-factor experiment analyzed using Analysis of Variance ( ANOVA ).
UniOTP series has 3 different models, which support different one-time-password generation mechanism, including time-based, event-based and challenge / response-based to provide two-factor authentication.

two-factor and acceptance
As a result of challenges with integration and user acceptance, true two-factor authentication is not yet widespread, although it can be found in certain sectors requiring additional security ( e. g. banking, military ).

two-factor and people
Frederick Herzberg's two-factor theory, a. k. a. intrinsic / extrinsic motivation, concludes that certain factors in the workplace result in job satisfaction, but if absent, they don't lead to dissatisfaction but no satisfaction. The factors that motivate people can change over their lifetime, but " respect for me as a person " is one of the top motivating factors at any stage of life.
Critics of Herzberg's theory argue that the two-factor result is observed because it is natural for people to take credit for satisfaction and to blame dissatisfaction on external factors.
The UniMate TRRS and USB DUAL-Enabled token increases flexibility by enabling people to leverage the security benefits of two-factor authentication in either a computer or a smart phone.

two-factor and with
With two-factor authentication, something the user ' has ' is also used ( e. g. a security token or ' dongle ', an ATM card, or a mobile phone ); and with three-factor authentication, something the user ' is ' is also used ( e. g. a fingerprint or retinal scan ).
The two-factor, or motivation-hygiene theory, developed from data collected by Herzberg from interviews with a large number of engineers and accountants in the Pittsburgh area.
An alternative option available to users interested in using Steam Guard is two-factor, risk-based authentication, through the use of a one-time verification code sent to a verified email address associated with the Steam account.
Disconnected tokens are the most common type of security token used ( usually in combination with a password ) in two-factor authentication for online identification.
A 2008 survey of over 120 U. S. credit unions by the Credit Union Journal reported on the support costs associated with two-factor authentication.
Faced with regulatory two-factor authentication guidelines in 2005, numerous U. S. financial institutions instead deployed additional knowledge-based authentication methods, such as shared secrets or challenge questions, only to discover later that such methods do not satisfy the regulatory definition of " true multifactor authentication ".
Often, strong authentication is associated with two-factor authentication or more generally multi-factor authentication.

two-factor and such

two-factor and .
uses two-way ( two-factor ) analysis.
To prevent this, two-factor authentication can be used.
When elements representing two factors are required for identification, the term two-factor authentication is applied — e. g. a bankcard ( something the user has ) and a PIN ( something the user knows ).
User-created remote access VPNs may use passwords, biometrics, two-factor authentication or other cryptographic methods.
The content theory includes the hierarchy of needs from Abraham Maslow and the two-factor theory from Herzberg.
After two-factor authentication involving a PIN and One-Time-Password, the consumer's mobile account is charged for the purchase.
It uses time and / or challenge based two-factor authentication and can be used to sign electronic transactions.
The authors note two-factor theory involves stark disapproval of the clients ’ “ irrational behavior ” ( p. 350 ); this notably negative outlook could explain the results.

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Are Rising Imports a Boon or Bane to the Economy?

This article appeared in The Washington Times on September 8, 2010.

It has become conventional wisdom in reporting on the economy that rising imports and a growing trade deficit are bad signs for growth. As recent headlines warn us, “Economic growth slowed by trade gap” (Washington Post) and “Wider Trade Gap Signals Weak Growth” (Wall Street Journal).

Such reporting flows unavoidably from Keynesian logic. If we import a million shoes to satisfy domestic demand, that’s a million shoes we no longer need to make ourselves. When demand “leaks” abroad, the economy grows more slowly and it creates fewer jobs.

Or so we are told.

Those journalists and the experts they quote who tout this theory should get their heads out of the theoretical clouds and look at what really goes on in the economy. All the evidence points to the fact that rising imports and a growing trade gap, far from being drags on growth, are among the surest signs that the economy is expanding.

A major flaw of the Keynesian view is that it neglects the supply‐side role of imports. More than half of what we import consists of goods consumed by producers — capital machinery, raw materials, parts and other intermediate inputs. Those imports help us produce more, not less. This is one reason why, over the past year, imports of manufactured goods have been rising along with domestic manufacturing output.

In the long run, imports spur growth by forcing domestic producers to be more efficient and productive. Like competition generally, imports weed out the less‐productive domestic producers, leaving the market to more‐competitive U.S. companies. Those companies are better able to expand their share in global markets and create sustainable jobs with higher pay.

Obsession with the trade deficit also ignores the fact that the dollars we spend on imports quickly return to the United States. If they are not used to buy our goods and services, they are spent on assets, such as real estate, stocks and Treasury bonds. This inflow of capital also helps to fuel growth by keeping interest rates down and providing capital to build factories and expand output.

If the Keynesian worry about imports were justified, we should expect to see a negative correlation over time between the growth in imports and the growth of GDP. Rising imports would tend to be associated with weaker growth, and more slowly growing or falling imports with stronger growth.

In reality, rising imports are one of the surest indicators of stronger economic growth. After examining quarterly economic data from the U.S. Bureau of Economic Analysis going back to 1980, I found a strong positive correlation between the change in real imports to the United States and the change in real gross domestic product (GDP). Compared with a perfectly proportional correlation of 100 percent, the correlation between imports and GDP is a strongly positive 62 percent.

Politicians myopically focus on exports, but the correlation between rising exports and rising GDP is actually weaker, at 45 percent, than the connection between imports and GDP. If President Obama wants to promote more robust economic growth through trade, he should consider complementing his National Export Initiative with a companion National Import Initiative. The only other major categories of economic activity that correlated more strongly to GDP growth than imports were personal consumption expenditures, at 66 percent, and gross private domestic investment, at 80 percent.

You don’t need to analyze a spreadsheet to see the positive connection between imports and economic growth. Think back to the 1990s, the “Goldilocks economy” from 1992 to 2000, when growth was strong, jobs plentiful and inflation low. That was also a time of surging imports and rising trade deficits. Again, in the middle of the George W. Bush presidency, imports resumed their robust growth and the trade gap grew for five straight years. During that same period, 2002 – 2007, the economy expanded and the unemployment rate fell from 5.7 to 4.4 percent.

In contrast, imports slow and the trade deficit invariably shrinks during periods of recession. From 2007 to 2009, during the “great recession,” the trade deficit fell by half as domestic demand and imports plunged, while unemployment soared. Indeed, 2009 was one of only half a dozen years in the past half century in which the value of imports actually decreased from the previous year. The others were 1961, 1975, 1982, 1991, and 2001. According to our Keynesian friends, those should have been robust years for economic growth and job creation. In fact, each of those years was marked by recession.

The Keynesian believers among us may be wishing for a decline in imports, but for millions of Americans struggling in the real economy such a decline would be a curse.


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Occupational Therapy Doodle

When discovering that your child may have differing needs or abilities, you instantly can become saturated in a world of new lingo and jargon, three letter acronyms, and medical language that might leave you with more confusion and uncertainty.  We hope the following list will help clarify some of the common language you might hear within therapeutic settings.

Adaptive response: an appropriate action in which the individual responds successful to some environmental demand.  Adaptive responses require good sensory integration, and they also further the sensory integrative process.

ADL/ADLS: activities of daily living.  Examples include: bathing, grooming, dressing, hygiene related skills, etc.

Bilateral coordination: the ability to use both sides of the body at the same time in a controlled and organized manner. This can mean using both sides of the body to do the same thing, as in using a rolling pin or using alternating movements, like when walking or climbing stairs, or when using different movements on each side, like when cutting with scissors while holding the paper with the other hand.

Dyspraxia: poor praxis or motor planning that is often related to a decrease in sensory processing.

Eye-hand coordination: the efficient teamwork of the eyes and hands, necessary for activities such as playing with toys, dressing, and writing.

Extension: the act of straight in the neck, back, arms, or legs.

Fine Motor: referring to movement of the muscles in the fingers, toes, eyes, and tongue.

Fine motor skills: the skilled use of one’s hands- the ability to move the hands and fingers in a smooth, precise, and controlled manner. Fine motor control is essential for handling of classroom tools and materials- may also be referred to as dexterity.

Flexion: the act of bending or pulling in a part of the body.

Gravitational insecurity: extreme fear and anxiety that one will fall when one’s head position changes.  This is often related to poor processing of vestibular and proprioceptive information.

Gross Motor: movements of the large muscles of the body.

Gross motor skills: coordinated body movements involving the large muscle groups, for example, running, walking, hopping, climbing, throwing, and jumping.

Hypersensitivity: oversensitivity to sensory stimuli, characterized by a tendency to be either fearful and cautious, or negative and defiant.

Hypersensitive to movement: a sense of disorientation and/or avoidance of movement that is linear and/or rotary.

Hyposensitivity: the under-sensitivity to sensory stimuli, characterized by a tendency to either crave intense sensations or to withdraw and be difficult to engage.

Interoception: the sense that provides information about the internal state of our body, i.e. how our body is feeling on the inside. For example, awareness of hunger, thirst, pain, need for bathroom, or need for rest.

Motor control: the ability to regulate and motor the motions of one’s muscle group to work together harmoniously to perform movements. For example, the ability to move your toothbrush across your teeth requires a particular amount of control in your hand and arm.

Motor coordination: the ability to use multiple body parts for a particular action.  For example, dribbling a basketball requires the coordination of your arms and hands to bounce the ball, coordination of your eye muscles to come together and apart in watching where the ball is moving, and coordination of your feet and legs to move your body around the basketball court.

Motor planning: the ability to conceive of, organize, sequence, and carry out an unfamiliar and complex body movement in a coordinated manner.  Also referred to as praxis.

Muscle tone: the degree of tension normal present when one’s muscles are relaxed or in a resting state.

Nystagmus: a series of automatic, back-and-forth eye movements.

Prone: the horizontal body position with the face and stomach downward.

Proprioception: the perception of sensation from the muscles and joints. It tells the brain when and how muscles are contracting or stretching, and when and how the joints are bending, extending or being pulled or compressed. This information enables the brain to know where each part of the body is and how it is moving.

Rotary movement: turning or spinning in circles

Self-Regulation: the ability to control one’s activity level and state of alertness, as well as one’s emotional, mental, or physical responses to senses; self-organization.

Sensory Defensiveness: child’s behavior in response to sensory input, reflecting over-reactions or a low threshold to a specific sensory input

Sensory discrimination: the ability to perceive various aspects of sensation (light, touch, texture, smell, taste, etc)

Sensory input: the streams of neural impulses flowing from the sense receptors in the body to the spinal cord and brain.

Sensory integration: the process by which we receive information through our senses, organize this information, and use it to participate in everyday activities.

Sensory integrative dysfunction: the inefficient neurological processing of information received through the senses, causing problems with learning, developmental, and behavior.

Sensory Modulation: the process by which the brain takes in various forms of sensory inputs and sorts them all out. It is the ability to effectively regulate the degree to which one is influenced by various sensory inputs. Efficient sensory modulation allows the central nervous system to regulate such things as attention and arousal level by enabling one to attend to important stimuli, filter out irrelevant stimuli, and modify the amount of stimuli one is exposed to.

Sensory Registration: initial awareness of a single input; assigning value and emotional tone to a stimulus.

Supine: the horizontal body position with the face and stomach upward.

Tactile: pertaining to the sense of touch on the skin. Also refers to various qualities attributed to touch, including detecting pressure, temperature, light touch, pain, and discriminative touch

Tactile defensiveness: a sensory integrative dysfunction in which tactile sensations create negative emotional reactions.  It is associated with distractibility, restlessness, and behavior problems.

Vestibular system: the sensory system that responds to the position of the head in relation to gravity and accelerated and decelerated movement; it integrates neck, eye, and body adjustments to movement.

Visual discrimination: differentiating among symbols and forms, such as matching or separating colors, shapes, numbers, letters or words

Visual Figure Ground: differentiation between objects in the foreground and background

Visual motor: referring to one’s movements based on the perception of visual information

Visual motor skills: the ability to take in visual information, process it, and be able to coordinate your physical movement in relation to what has been viewed. It involves a combination of visual perception and motor coordination. Difficulty with visual motor skills can result in inaccurate reaching, pointing, and grasping of objects, as well as difficulty with coping, drawing, tracing, and cutting.

Visual perception: the ability to perceive and interpret what the eyes see.

Visual perceptual skills: the ability to interpret and use what is seen in the environment. Difficulties in this area can interfere with a child’s ability to learn self-help skills like tying shoelaces and academic tasks like copying from the blackboard or finding items in a busy background.

Visual-Spatial Processing Skills: perceptions based on sensory information received through the eyes and body as one interacts with the environment and moves one’s body through space. Including: depth perception, directionality, form constancy, position in space, spatial awareness, visual discrimination, and visual figure-ground.

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Buoyancy Calculator

Easily calculate the buoyant force experienced by a body / object submerged in a fluid given the fluid's density and the displaced volume. The buoyancy calculator also outputs the weight of the displaced fluid and supports both metric and imperial measurement units. The calculator can be used for both fully-submerged and partially-submerged objects.

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What is a buoyant force?

Buoyancy is an upward force exerted by a fluid in direction opposite to a downward force such as gravity and thus it counters (partially or fully) the weight of an object immersed in the fluid. Pressure increases with depth due to the weight of the increasing quantity of fluid above it so the further down an object has to push its way, the more resistance it faces. This is why if you push an object down slightly and remove your hand, it will float back up due to the greater buoyancy force encountered at the new depth. Similarly, the pressure at the bottom of a submerged object is greater than that at the top of the object which results in a pressure difference responsible for the net upward force on the object we call buoyancy force. The buoyancy principle was famously discovered by Archimedes and can be stated approximately as such: "Any object, wholly or partially immersed in a fluid, is buoyed up by a force equal to the weight of the fluid displaced by the object".

The magnitude of the buoyancy force is measured in Newtons and is equivalent to the weight of the fluid displaced by the object, which is also proportional to the pressure difference. As result, if a submerged object has greater average density than the fluid it tends to sink ("fall into water"), otherwise the buoyancy force keeps it afloat. Buoyancy exists only as long as there is a force pressing the object into the fluid, e.g. gravity forcing a human body into sea water.

Buoyant Force Formula

The formula for the buoyant force (a.k.a. buoyancy equation) (B) is expressed in terms of the body's volume or more precisely the volume of fluid displaced by the body Vdisp in m3, the density of the fluid in question (p, in kg/m3) and the gravitational (or equivalent) force g in m/s2 by the equation:

buoyant force

The result is the buoyant force in Newtons. Straightforward transformations can be performed to solve for any of the variables. In our calculator you can enter gravity both in m/s2 and as g-units where 1g = 9.80665 m/s2 is the standard acceleration due to Earth's gravity at sea-level.

The formula is valid for homogenous materials where the density of all points in the object equals its total mass divided by its total volume. Determining the density of a gas or liquid may require the use of specialized equipment like a hydrometer, a dasymeter or a Coriolis flow meter. If the object is of heterogenous density a piece-wise approach may be preferred.

Calculating the buoyant force - examples

Example 1: A ship has a displacement tonnage (the total weight of the volume of water a ship "displaces" when it is partially-submerged in the water) of 10 tonnes. Let us say it is sailing at sea and we know that sea-water density is approximately 1024 kg/m3, on average. What is the buoyant force experienced by the ship?

First, convert the tonnes to kg by multiplying by 1,000 to get 10,000 kg. Then use the buoyant force equation above to get B = 10,000 · 1024 · 9.80665 = 100,518,162.5 Newtons (N) or around 100.52 MegaNewtons (MN).

Example 2: If the density of water in a swimming pool is 1,000 kg/m3 and we have a life saver which weighs 1kg and has a volume of 0.12 m3, what is the maximum weight of a person so they can safely float using this life preserver?

In this case we do not even need to know the buoyant force to estimate the maximum weight the life preserver can support on top of its own weight. We simply multiply the volume displaced by the life preserver which is 0.12 m3 (assuming no caving in) by the density of the water to get the total weight which would just barely float: 0.12 · 1,000 = 120 kg. Subtracting the weight of 1 kg and allowing, say, 5-10 kg for comfortable floating such a life preserver should be able to keep afloat in fresh water a person weighing up to 110 kg.

life saver floating water

The above examples are just educational ones, obviously. Our buoyancy calculator will automatically convert for you any units to the base SI units.

Density of water and other common liquids

The density of a material or substance varies with its temperature and pressure, which is typically negligible for solids and liquids but is much greater for gases. Increasing the pressure on a substance decreases its volume and increases its density. Increasing the temperature generally decreases the material density and increases its volume (increasing denominator in the formula). A substance with uneven temperature will also have varying pressure and density.

As an example of how water density changes with temperature, at 1 atm pressure water has density of 983.854 kg/m3 at -30 °C, 999.9720 kg/m3 at 4 °C and only 958.4 kg/m3 at 100 °C (boiling temperature at sea-level). When the substance is a mix, its density changes. Sea-water density is, on average, about 1,025 kg/m3. Here is a reference table for some commonly encountered fluids:

Density of common fluids
FluidDensity (kg/m3)
Air 1
Water 998
Sea-water 1020-1050
Ethanol 789
Alcohol 800
Milk 1020-1050
Plant Oil (Cooking Oil) 910-920
Fuel Oil 890
Auto Oils 880-940

Where ranges are given the density and thus buoyancy varies depending on the particular type of substance or mixture. E.g. average sea-water density is 1024 but it can reach 1050 under certain conditions like high pressure and high concentration of salts.

[2] "The International System of Units" (SI) (2006, 8th ed.). Bureau international des poids et mesures pp. 142–143. ISBN 92-822-2213-6

Cite this calculator & page

Georgiev G.Z., "Buoyancy Calculator", [online] Available at: https://www.gigacalculator.com/calculators/buoyant-force-calculator.php URL [Accessed Date: 19 Jan, 2020].


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Apple’s motion to vacate the All Writs Act assistance order: Has Apple chosen the best “test case”?

Here it is.

On first read, I think it has some very strong sections, including, in particular, its description of the efforts Apple would have to undertake to create and secure the software necessary to disable the “auto-erase” feature on the iPhone in question, and its explanation of how the assistance required by the court’s order goes far beyond how the All Writs Act has previously been used (and is easily distinguishable from the assistance at issue in the leading AWA precedent, United States v. New York Telephone).

The brief also includes some much weaker arguments, such as the paper-thin Free Speech and Due Process arguments at the tail end, and its lead argument — that Congress has already precluded this sort of order in CALEA. (It is true, as Apple argues, that “in CALEA, Congress decided not to require electronic communication service providers, like Apple, to do what the government seeks here,” and that CALEA itself expressly “does not authorize” law enforcement to compel Apple to write such software. It does not follow, however — not even close — that “Congress declared via CALEA that the government cannot dictate to providers of electronic communications services or manufacturers of telecommunications equipment any specific equipment design or software configuration,” even if such an order would otherwise have been permissible under the All Writs Act. The brief’s related “separation of powers” argument (p. 18) is weaker still: the court here is in no way purporting to “repurpose” CALEA, or to impose generally applicable obligations on all companies similarly situated to Apple.)

The strongest part of the brief, at least at first glance, is where Apple argues that the order would be unreasonable, under New York Telephone’s AWA “test,” because Apple has a compelling business interest in not creating and facilitating the use of software that would “invade or control the private data of its customers,” and that would “undeniably threaten” the data protection systems of customers who depend on those systems to store their most confidential data.

But there’s the rub, at least in this case: Here, Apple’s customer — the entity to which it arguably owes some loyalty and whose trust it does not wish to betray — is the San Bernardino County Department of Public Health (Farook’s employer), which owns the iPhone in question. The SBCDPH has consented to the government’s search, and apparently it agrees with the FBI that Apple should create and implement the software that would disable its iPhone’s “auto-erase” function (which would in turn make it possible for the government to find the passcode through “brute force” searches). In this case, therefore, Apple’s concerns would appear to be misplaced — which in turn argues in favor of the reasonableness of the order.

Apple quite understandably emphasizes the fact that “the government has filed multiple other applications for similar orders, some of which are pending in other courts,” and that “state and local officials [have] publicly declared their intent to use the proposed operating system to open hundreds of other seized devices.” Presumably, in some or all of those other cases, Apple’s customers do not wish for Apple to disable their iPhone’s “auto-erase” functions, which would, arguably, be a betrayal of the implied promises of security that Apple sold to them. Apple’s “unreasonableness” argument under the AWA would have much greater force in such a case.

The court’s order in the San Bernardino case, however, could be limited — both by its terms and in its precedential effect — only to those cases in which the iPhone owner wants Apple to disable the auto-erase function, so that the owner itself can get at the information on the iPhone, which it is lawfully entitled to access in the first instance.

To be sure, the phone owner’s consent would not, in and of itself, settle the “reasonableness” question under the AWA, particularly if my co-blogger Julian Sanchez is correct that there would be a significant risk that Apple’s new “key” would be hacked and compromised, with deleterious effects for many phones beyond the one in the FBI’s possession. According to Julian, such a key “almost certainly [would be] stored [by Apple] in secure vaults, on a Hardware Security Module that makes it difficult or impossible to copy the key itself off that dedicated hardware, and likely protected by elaborate procedures that have to be followed to authenticate major new software releases.” Even so, he worries that “[i]f your adversaries realistically include, say, the Chinese and Russian intelligence services — and for Apple, you’d better believe it — it’s a serious enough security problem to guard against exfiltration or use of that Holy Grail private key. Doing the same for a continuously updated and deployed hacking tool is likely to be hugely more difficult.” [This is the way Apple puts the same point (I think): “Keeping and maintaining the compromised operating system and everything related to . . . forc[es] Apple to take on the task of unfailingly securing against disclosure or misappropriation the development and testing environments, equipment, codebase, documentation, and any other materials relating to the compromised operating system. Given the millions of iPhones in use and the value of the data on them, criminals, terrorists, and hackers will no doubt view the code as a major prize and can be expected to go to considerable lengths to steal it, risking the security, safety, and privacy of customers whose lives are chronicled on their phones.”]

I’m no expert in such things, but this strikes me as perhaps the most important question in the case: What are the risks that the software, despite Apple’s best efforts to secure it, would be compromised by others? (The cost to Apple to prevent such an occurrence is presumably less of an issue — or at least it would be if the government were willing to foot the bill, as I presume it is.) If those risks are minimal, then perhaps Apple would be much better off making its AWA “unreasonableness” argument in a case where the owner of the iPhone does not consent to Apple’s assistance in disabling the “auto-erase” function.

P.S. The most intriguing argument in Apple’s brief is the suggestion (p. 30) that the FBI should not be permitted, under the All Writs Act, to put Apple to the task of creating the new software unless the government can represent that the NSA can’t offer the necessary assistance, which “might obviate the need to conscript Apple.”

About the Author(s)

Marty Lederman


Academic journal article Journal of Money, Credit & Banking

Testing Term Structure Estimation Methods: Evidence from the UK STRIPS Market

Academic journal article Journal of Money, Credit & Banking

Testing Term Structure Estimation Methods: Evidence from the UK STRIPS Market

Article excerpt

THE TERM STRUCTURE of interest rates defines the array of discount factors on a collection of default-free pure (zero-coupon) discount bonds that differ only in their time to maturity. It is used for a number of purposes. For example, it can be used to set the price of new issues of default-flee debt, to set the interest rates on intragovernmental loans, or to test the theories of the stochastic evolution of the term structure (e.g., Cox, Ingersoll, and Ross 1985).

The prices of government bonds, known as gilts in the United Kingdom, are generally used in the estimation of the term structure of interest rates. Following the introduction of the gilt STRIP facility in December 1997, which permitted the separate trading of the cash flows of a selection of conventional (coupon-bearing) gilts, direct observation of the term structure of interest rates became possible. The yield to maturity on a stripped cash flow, which is its per period average rate of return until maturity, defines the unique default-free interest rate for that maturity. Unless there exists a continuum of zero-coupon bonds across the maturity range, however, there will be a need to use some form of interpolation method whenever interest rate estimates are required for dates that do not coincide with existing cash flows. The aim of this study is to compare a selection of interpolation methods in the case of the UK gilt STRIPS market.

Interpolation methods were introduced into the term structure literature long before zero-coupon bonds were first traded. At that time, it was necessary to use coupon-bearing bonds to estimate the term structure, and the use of interpolation methods met twin objectives. In addition to the desirability for interpolation, the use of such methods would, in many cases, make estimation feasible. In the absence of arbitrage, the prices of all coupon-bearing bonds, [P.sub.i], are related to the zero-coupon yield (spot interest rate) curve, [y.sub.t], by

where the discount function, d(t) = exp(-ty(t)), is the price of a unit payoff at date t and where bond i pays a periodic coupon at rate [c.sub.i] and repays its face value, [F.sub.i], at date [n.sub.i]. If the number of bonds equals the number of payment dates, the term structure can be exactly identified by solving equation (1) simultaneously for all bonds. In the case where there are more bonds than payment dates, equation (1) is overidentified, but an approximate fit can be obtained by using, for example, least squares. Carleton and Cooper (1976) successfully employed this method by selecting a sample of around 30 bonds that only incurred four payment dates per year and had a maximum maturity of 7 years. In most bond markets, however, the number of payment dates greatly exceeds the number of bonds available, and so some means of reducing the dimensionality of the term structure (to less than the number of bonds) is needed. The solution, first suggested by McCulloch (1971), is to approximate the term structure with a low-dimensional function, which depends on a small number of parameters. This function, being defined across the maturity spectrum, simultaneously undertakes the interpolation task.

Since the pioneering work of McCulloch (1971, 1975), a huge literature has grown up devoted to both innovating and comparing alternative approximation functions. (1) With few exceptions, this literature has used coupon-bearing bonds to conduct the empirical analysis. With coupon-bearing bonds the comparison of alternative approximation functions is made a more complex task as the functions are both making the estimation feasible and providing the interpolation service. With zero-coupon bonds, estimation of at least some points along the term structure is always feasible, and so the approximation function is only providing the interpolation service. The feasibility and interpolation benefits from the use of approximation functions do not arise without a cost. …

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A Pineywood cow, one of the earliest breeds to come to North America with the Spaniards in the 1500sJeannette Beranger / The Livestock Conservancy

Crossing the vast network of American roadways, drivers pass through fields of corn, soy, and wheat. They see power lines, roadside World’s Largests, kitschy diners—and cows. About 9 million dairy cows occupy the nation’s rural landscape, and of those, 94 percent are the familiar black-and-white Holsteins, a breed so archetypal that even the cow emoji is a Holstein. But this wasn’t always the case.

When cattle first became a part of American agriculture, New World colonists raised rugged cows of diverse stock. Some were native to North America, others brought from Europe, but all were dual- or tri-purpose breeds—good for milk, meat, and draft power. Unlike today’s cows, who are often cornfed and heavily medicated in vast indoor feeding facilities, these “heritage” cows were scrappy, able to thrive outside on unmanaged grasses. To this day, heritage cattle are naturally disease-resistant, produce offspring without human intervention, and can be milked well into their teen years. And they’re slowly going extinct.

Over the past century, the rise of the Holstein cow has decimated many native and heritage breeds. Fewer than 4,000 Guernsey cattle were registered last year, down from the 44,000 tallied in a 1930 U.S. Department of Agriculture report. The 6,000 Red Poll cows accounted for in the 1920s have dropped to just 524, and only 245 registered Milking Shorthorns remain today, falling from several thousand as recently as the 1970s.

A small group of conservationists are scrambling to keep these Landrace cattle around, but they’re up against a dairy industry dependent on high-output Holsteins, not to mention thousands of farmers whose livelihoods hang in the balance. Most commercial dairy farms are in no position to take a chance on ancient breeds as they fend off volatile milk prices and decreased demand. But if the last of the nation’s heritage cows disappear, it might be the dairy industry that suffers most.

* * *

Holsteins came to the United States from Holland in the late 1800s, nearly four centuries after the earliest known appearance of American heritage cattle. Introduced in Massachusetts in 1852, Holsteins were coveted for their distinctive spotted markings and the astonishing volume of milk they could produce. They left other breeds in the dust at a time when demand for liquid milk was steadily rising due to pasteurization and refrigeration. Artificial insemination became commonplace around this time, too, which enabled targeted Holstein breeding.

Over the decades, farmers across America, under pressure to meet the needs of a rapidly growing population, crossbred heritage cattle with the impressive Holsteins to increase production. Other heritage cows were lost in the 1980s, slaughtered for beef in a Department of Agriculture herd reduction aimed at raising milk prices.

A green screen
The SVF Foundation, a Rhode Island-based organization that preserves rare livestock, is one institution performing the controversial practice of cryopreservation to stockpile heritage-cow genes. (Sarah Bowley)

“We operate in an economic model that pushes to maximize output,” says Sam Frank, a cheesemaker researching heritage breeds. “That’s why your typical conventional dairy operation normally involves hundreds, if not thousands, of high-yielding Holstein cows that spend all their lives chained to a stall eating grain and pumping out milk.”

Frank is part of a cohort of dairy-industry advocates fighting to keep rare breeds on American farms. He and these other die-hards paint a dire picture of a world without the cows, though they all have different reasons for bearing the heritage cross. The most pressing is what the loss of this genetic diversity could mean for a warming planet.

As the world’s population continues to balloon and environmental changes endanger global food supplies, resilient cows offer numerous advantages. Take the heat-tolerant Pineywood breed, one of the earliest breeds to come to the United States with the Spaniards in the 1500s. Pineywoods adapted to the American southeast and are able to thrive in extremely hot conditions, a beneficial trait to preserve amid rising temperatures. Most heritage breeds are also disease-resistant—useful in the event of widespread epidemics—and have excellent foraging abilities, which means they could survive on grass should something happen to corn crops.

Other conservation campaigns have been motivated less by environmentalism than by the desire to preserve history and tradition. In the 1970s, Vermont’s Randall cattle got down to a single herd when the farmer Everett Randall passed away. His family, ill-equipped to care for them, planned to disperse the remaining 15 cows. But when the Tennessee farmer Cynthia Creech got word of their plight in Small Farmer’s Journal, she was so moved that she researched the breed, bought all the animals, then restored them to health on her farm before moving herself and the cattle back to their hometown in Vermont.

The ranks of these conservationists are diverse, including activists like Frank, breeding associations across the United States and Canada, research facilities, and an organization called the Livestock Conservancy, which was created in the 1970s when a few historians had trouble locating an American Milking Devon cow for a commemorative display in Massachusetts. The conservancy, based in North Carolina, protects heritage cows by tracking their status and conducting rare-breed rescues to retrieve dwindling herds in areas threatened by predators or development. The breed associations focus on connecting what few heritage farmers are left. The American Milking Devon Cattle Association, for instance, maintains a bank of semen, which they sell to heritage breeders.

Cryopreservation, embryo transfer, and semen banks are tempting ways to grow these breeds quickly, but the practices are controversial within the heritage community for the threat they pose to gene pools that are already very limited. Inbreeding isn’t much better than shrinking biodiversity, so conservationists have to strike a balance. “If the agricultural landscape becomes more diversified, it will be less susceptible to possible collapse—like in the example of the Irish Potato Famine,” says Ryan Walker, the marketing and communications manager for the Livestock Conservancy.

Walker’s reference to Ireland’s dependence on a potato strain that was hit by blight in the mid-1800s is one that conservationists raise often. “Heritage breeds are our insurance policy against this type of collapse,” he says.

* * *

Despite these concerns about extinction, struggling dairy farmers often don’t have the time or resources to consider breeding cows who produce less milk than Holsteins. Milk prices have been in a slump for years, leaving commodity dairy farms on tight budgets with razor-thin margins. Many farmers take second jobs.

“Farmers face the same bills the rest of us do, with food, housing, health care, children going to school,” says Drew Conroy, a professor and farmer who used to head up the American Milking Devon Cattle Association, an organization of Devon breeders located throughout New England. Conroy owns Devons, but says that without his teaching job, he wouldn’t be able to make a living off of the cows alone. For the full-time Holstein farmers producing the bulk of the U.S. milk supply, there is even less room for experimentation with rare breeds.

Conceivably, there are ways around this. The European Union, for instance, subsidizes heritage-cattle husbandry as part of an effort to protect Europe’s considerably older cultural history. In Sam Frank’s research abroad, he met a farmer in Sicily getting 400 euros per cow per year, and another in Ireland getting 200 euros. The U.S. government isn’t ignoring breed preservation—in 1999, the USDA’s Center for Agricultural Resources Research in Fort Collins, Colorado, partnered with the Livestock Conservancy to study livestock security and adaptability to climate change. But while this research is valuable, it doesn’t incentivize risky breeding for the average American dairy farmer.

A more lucrative approach for farmers, conservationists hope, might be a ramped-up marketing push akin to the campaign of the heirloom tomato, tapping into the farm-to-table ethos that has come to dominate the American grocery store. “Encouraging consumers to buy heritage-breed products increases demand and encourages farmers to breed more,” says Walker. “We always say, ‘you have to eat them to save them.’”

American Milking Devon cows
The American Milking Devon, a breed brought to North America in the 17th century (Jeannette Beranger / The Livestock Conservancy)

Many heritage pork products are already on shelves across the United States, and heritage dairy has begun cropping up as well. But using the “heritage” stamp as a branding tool has its drawbacks. With the word itself able to bring in higher profits, the market is vulnerable to infiltrators trying to sell less-than-heritage products. Farmers are again crossbreeding Holsteins and heritage cattle, not just to increase output this time, but to gain access to the coveted heritage moniker—a practice that purists like Frank and the Randall Cattle Registry folks are staunchly against.

Vermont’s Sweet Rowen Farm has adopted this practice, blending Randalls with higher-producing breeds to create what they’re calling “Vermont Heritage Linebacks.” Katt Tolman, the farm’s herd manager, is unfazed by criticism that her mixed herd is bad for the heritage movement. “I personally don’t see the benefit of keeping any breed or race ‘pure.’ Nature is not stagnant or rigidly segregated,” she says. “It seems to me that the rare-breed associations are hurting their own cause by being so rigidly exclusive. If rare breeds are not allowed to be fluid and adaptive to current agricultural needs, they will only be the playthings of the rich and idle.”

Whether or not conservationists agree with these methods, younger farms like Sweet Rowen might be the most likely stewards of heritage breeds. They represent a generation of farmers less invested in the old commodity system, better equipped to take a chance on risky branding. “Converting to heritage requires a different husbandry approach, so in some ways it’s easier for farmers to start from scratch,” says Walker.

If young farmers raise heritage breeds with the intent to preserve them, they could shepherd at-risk cows away from direct competition with Holsteins and steer them toward organic consumers, who are often more willing to spend extra on environmentally minded products. But there’s still a chance that these young, experimental farmers will lean on crossbreeding to increase volume—in which case pure heritage populations could continue to drop, or disappear.

If new farmers do find success, though, buoyed by a hip label and unfettered by decades of investment, longtime Holstein farmers get stuck with the short end of the marketing stick. “We tend to blame the farmer for the practices that the free market asks/demands of them,” Richard McCarthy, the executive director of the nonprofit Slow Food U.S.A., said in an email. “Holsteins are marvelous, beautiful creatures who produce volume. If volume/price is the only concern, then they win. If we as consumers and as a society start to value other attributes, other breeds can join in the winnings.”