Editors' Introduction: Why Formal Learning Theory Matters for Cognitive Science

  • Note: Nick Chater was supported by ERC Advanced Research Grant “Cognitive and Social Foundations of Rationality.”

Correspondence should be sent to Sean Fulop, Department of Linguistics PB92, California State University Fresno, Fresno, CA, 93740. E-mail: sfulop@csufresno.edu

This article reviews a number of different areas in the foundations of formal learning theory. After outlining the general framework for formal models of learning, the Bayesian approach to learning is summarized. This leads to a discussion of Solomonoff's Universal Prior Distribution for Bayesian learning. Gold's model of identification in the limit is also outlined. We next discuss a number of aspects of learning theory raised in contributed papers, related to both computational and representational complexity. The article concludes with a description of how semi-supervised learning can be applied to the study of cognitive learning models. Throughout this overview, the specific points raised by our contributing authors are connected to the models and methods under review.

Formal learning theory, broadly construed, is a diverse collection of approaches to the mathematical modeling of learning. From the point of view of cognitive science, formal learning theory can provide constraints on what is learnable by different types of idealized mechanism. Moreover, in some cases, specific algorithms discussed may be part of the cognitive model. Therefore, such modeling could potentially be relevant to human learning, either by modeling the learning algorithm directly or by deriving theoretical results which constrain what any learning system may possibly achieve given certain data. In contrast, many theoretical discussions and computational frameworks for understanding learning in cognitive science are often not appropriately related to theoretical findings, so that, for example, whether a particular model can, in principle, scale up to more complex cases may be difficult to assess. Conversely, when not directly tied to cognitive scientific questions (or practical challenges in machine learning), formal learning theory can become a rather specialized mathematical activity, with no clear application. This disconnect between two disciplines that can productively inform each other more fully deserves some remedy, and it is our hope that the papers within the present topic go some way toward fostering a greater connection.

Although cognitive science is most canonically concerned with the construction of computational models of specific cognitive phenomena (including learning of all kinds, and of course language acquisition), there remain fundamental questions about the capabilities of different classes of cognitive model, and about the classes of data from which such models can successfully learn. In view of this, formal learning theory has the potential to play a role within cognitive science analogous to that played by theoretical computer science with respect to applied computing. Thus, we suggest that the analysis of learning in cognitive science has a rich vein of theoretical work to draw upon, but too often the literature describes specific computational learning simulations that are not accompanied by, or situated within, any theoretical analysis.

Theoretical analysis of learning is frequently discussed under the heading of “inductive inference”—viewing learning as a type of inference in which the “premises” typically consist of observed data (which might be perceptual input, reward signals from the environment, linguistic materials, and so on). Early formal work on inductive inference attempted to model the process by extending classical logic to inductive logic, for example Carnap (1950), building on probability theory. Although still pursued in some fields (Anshakov & Gergely, 2010; Milch et al., 2007; Muggleton, 1990), research has typically shifted away from the powerful but often intractable representational machinery of formal logic, to models of learning defined over much more restricted representational formalisms. Thus, interest often focuses on relatively restrictive problems, such as learning categories from examples, or learning what sentences are allowed in a language from examples (or, even more abstractly, learning potentially infinite sets of numbers from examples, and perhaps also nonexamples, labeled as such).

Various formal learning models have gradually come to the fore, including Bayesian inference, the statistical inference model codified in Solomonoff (1964a), the Probably Approximately Correct framework proposed by Valiant (1984), and the “identification in the limit” model of (Gold, 1967). The now well-known “Gold's theorem” from the latter launched a vigorous debate in linguistics which is far from over. Its deceptive simplicity has led to its being possibly more often misunderstood than correctly interpreted within the linguistics and cognitive science community, as was richly documented by Johnson (2004). All of these learning models fall under the rubric of supervised, because they must use data that are in some sense labeled—either the learner assumes all examples are positive examples of one concept such as a language, or a more elaborate distribution of multiple labels can be provided, as with category learning. Another area of research involves induction of structured knowledge from unlabeled data—so-called unsupervised learning. This area encompasses such disparate enterprises as data clustering, independent components analysis, and self-organizing maps; it is generally difficult to measure the success of such unsupervised techniques. The use of unlabeled data in conjunction with labeled data has developed into an interesting subfield of semi-supervised learning. We will next present a brief overview of learning theory covering many subfields, attempting to touch upon all of the approaches that are applied in the contributed papers.

1. Preliminaries

A standard way to model a restricted learning problem is to begin by supposing a concept class C encompassing all of the things that are like the thing to be learned in some sense. For the commonly considered language learning problem, for example, a concept class might be all sets of expressions over a certain (predetermined) vocabulary of words. This just fixes the domain of discourse, in a sense—what kind of learning problem do we have. Then learning can be represented as an effort to formulate a correct hypothesis. This target concept is generally assumed to be a member of a hypothesis class, H, which is a restricted subset of the entire concept class. One way in which this notion of learning is restricted is that the aim is to infer the elements of the category or language, but not, for example, to reconstruct the rules or grammar defining the category or language. Theoretical results generally pertain to the learnability of hypotheses in various sorts of hypothesis classes. Just what is meant by “learnability” also needs to be specified—do we mean asymptotic learnability (in the limit of an unending sequence of examples), or is some bound on performance or complexity of the process also to be required? Another possibility is to allow approximation of the correct hypothesis, rather than insisting on precise learning.

The learning process proceeds as the examples from a set D illuminating the target hypothesis arrive in a sequence one by one. The example sequence may be limited to members of the target hypothesis, which are known as positive examples, or the example sequence may include elements from both inside and outside the target (the latter being negative examples), in which case each example must indicate whether it is positive or negative. Solomonoff's (1964a) idea was that learning can be modeled as “extrapolation” from the sequence of examples, that is, successful prediction of the next symbol in the sequence. This model of learning as prediction was expansively presented by Li and Vitányi (2008), where it was shown to subsume other formal approaches, but can in the end be viewed as a subspecies of Bayesian learning.

2. Bayesian learning

The Bayesian approach to learning takes as its starting point that probabilities can be interpreted as “degrees of belief” in a “learning agent” (whether a child is acquiring a language or a machine learning algorithm).1 The Bayesian learning method amounts to updating these degrees of belief on encountering new data (which is assumed to be known for certain), in line with the rules of probability theory. Specifically, such updating typically involves applying an elementary theorem of probability, known as Bayes's rule:

display math

Here, the various Hi are the individual hypotheses in H, while D is the set of observed examples, or “learning sample.” The formula states that the “posterior” probability Pr(H | D) of a particular hypothesis H conditioned on observations D is proportional to the product of the learner's “prior” belief in (probability assigned to) H with the probability Pr(D | H) of the learning sample conditioned on H. The latter value is commonly called by the (rather misleading) name of likelihood. The denominator of the formula, often called the Bayesian evidence, makes it an equation rather than a proportion; it is not always necessary to compute this in order to choose among different hypotheses.

As data are encountered piecemeal, this formula can be applied iteratively: Today's posteriors are tomorrow's priors. More precisely, at step n, the posterior probabilities of a number of different hypotheses are computed with Bayes's formula. Then at step + 1, the new example is considered, and the posteriors are adopted as new prior probabilities in a reapplication of the formula. This general scheme has become widely applied in cognitive models of learning (e.g., Tenenbaum & Griffiths, 2001) as well as in Bayesian statistics. But how should the initial priors—the ones used at the first step of the iteration—be chosen? Somewhat reassuringly, under some conditions (e.g., H is a finite class; Freedman, 1963), Bayesian inference converges to the “optimal” hypothesis eventually starting from almost any prior, but convergence can be much faster if we start with a “better” set of prior probabilities. If the hypothesis class is a countably infinite distribution such as an infinite class of languages, the situation becomes more subtle, and the prior can actually affect the ability to converge. In any case, practically effective Bayesian inference demands the selection of a good prior.

The problem of the initial priors is the main subject of the contribution by Jacob Feldman in the current issue. This article discusses the matter of how initial priors should be optimized before carrying out Bayesian learning. Feldman states that there have been two opposing views on this matter. The “classical” Bayesian approach stands against deliberately optimizing the initial priors from empirical results and instead advocates applying one's general knowledge of the scenario to assign priors according to reasonable beliefs. An example of this is the standard assumption of a fair coin, where the favored (most probable) hypothesis initially would assign probability 0.5 to each side. If a coin is, in fact, not fair, a Bayesian learning procedure will uncover this by adjusting the posteriors through a long sequence of coin tosses, and a different hypothesis assigning the unequal probabilities will gradually emerge. The ancient “principle of insufficient reason” is the classic course of last resort, refusing to favor any hypothesis initially (making all priors equal) when general knowledge is not useful. This approach is considerably generalized in the widely used maximum entropy principle in Bayesian methods (Jaynes, 2003).

An opposing view, known in the literature as empirical Bayes (Berger, 1985), advocates using empirical facts about the various hypotheses, derived from a direct experiment, to assign the prior probabilities. This approach has become quite commonplace in cognitive science, but Feldman argues that such an attempt to “optimize” priors empirically runs counter to the principles and philosophy of Bayesian inference. Such a procedure applied in the coin-tossing example would involve flipping the coin a number of times beforehand and using the observed ratio of heads to tails as the “true prior nature” of the coin. Feldman uses simple Monte Carlo simulations of classifier performance to demonstrate the ineffectiveness of this kind of prior determination, and that using priors with greater entropy—and thus less information—leads to improved classifier performance. This shows that “mere tuning does not optimize performance,” where performance of the learner in this context means classification performance from a certain training set. Feldman offers suggestions toward a compromise, by assigning prior probabilities from a continuum between the empirically determined and the generally guessed. This kind of study could be profitably connected to more theoretical results (Haussler, Kearns & Schapire, 1994) showing the dependence of the Bayesian learning curve on the accuracy of the assumed prior distribution; it has been established that the learning curve—quantified as the probability of a mistaken prediction as the learning sample expands—is optimal when the assumed prior equals the true prior (where the true prior is defined purely internally to the simulation).

3. The universal prior

Solomonoff (1964a) proposed another solution to the problem of the Bayesian priors—the universal prior distribution. His framework of learning as predicting the next symbol in the example sequence was subsequently developed into a workable scheme by Li and Vitányi (1993, 2008), who showed that although the universal prior distribution may be uncomputable, it is approximated well by using a universal semimeasure M for a probability distribution over all hypotheses—and this can be approximated, in the limit, by a computable process. The mathematical details are not for the faint-hearted, but in essence we can view the distribution defined by M as a mixture of hypotheses which assigns greater weights to the “simplest” ones (Li & Vitányi, 2008). The interesting thing about this methodology is that the universal semimeasure is defined in terms of the complexity of hypotheses, specifically quantified as the Kolmogorov complexity measuring the degree of randomness in a sequence. The Kolmogorov complexity of the learning sample can be measured as the length of the shortest computer program which describes the examples (in a given language, though the choice of language can be ignored for theoretical purposes, just as the choice of computer can be ignored in time- or memory-complexity analyses of computer algorithms). This aspect brings in the ancient principle of Occam's razor—find all hypotheses that fit the data, and then make predictions by applying the simplest explanation—which is often connected to Bayesian learning.

The current contribution from Anne Hsu, Nick Chater, and Paul Vitányi reviews the authors’ recent formal results in the preceding framework, concerning the possibility of learning natural languages from positive example sequences. This question is arguably among the most frequently debated cognitive issues in linguistics. The paper reviews findings showing the learner can learn successfully, according to various criteria, from positive evidence by favoring the grammar that provides the simplest encoding of the linguistic input.

4. Identification in the limit

Gold's (1967) framework of identification in the limit (i.i.l.) largely initiated the study of formal learning theory. In this scheme, we are given an unending sequence of examples (either all positive, or both positive and negative) and the task is to infer a function (language, grammar, etc.) from a predetermined hypothesis class. The learner does not have to recognize its own success—the criterion for successful learning is convergence of the learner's hypotheses to the single correct target in the limit of the example sequence. The key parameters in applications of this paradigm are the hypothesis class and the specific method of induction from the examples. Although not conceived as a probabilistic learning scheme, i.i.l. has been shown (Li & Vitányi, 2008) to be equivalent to a particular setup of a Bayesian learner.

A variation of the Gold paradigm that has been suggested on natural cognitive grounds is the model of a memory-limited learner. This is a learning algorithm which conjectures hypotheses based on the latest example received, together with the previous conjecture (Wexler & Culicover, 1980). It is useful to consider the contrast with the full Bayesian learner, which considers new data in the light of its current probability distribution over all hypotheses; the memory-limited learner, in contrast, remembers only its current “favorite” hypothesis and throws away all information about previous hypotheses. This general style of learning has also been developed into a formal definition of a U-shaped learner; this is a learning algorithm that goes from correct conjectures to incorrect ones and then back to the correct conjecture as the example sequence is received (Carlucci, Case, Jain & Stephan, 2007). Cognitive science researchers may find this familiar, since natural language learning, among other things, often proceeds along a U-shaped learning curve in the natural setting. The current contribution from Lorenzo Carlucci and John Case provides a timely overview of the formal theory of U-shaped learning and its technical requirements, as well as its features relevant to cognitive science. The paper also successfully defends identification in the limit (and variations) as a valuable model relevant to the study of learning within cognitive science.

5. Complexity of learning

In the models considered so far, the most basic criterion of successful learning is simply to converge upon the target hypothesis. In most interesting applications, there are a very large number of competing hypotheses, so it may not be generally feasible to converge to the target precisely. Practically feasible learning faces at least two constraints: that successful learning occurs given the amount of data available to the learner (e.g., the amount of linguistic input received by the child); and that the calculations involved in such learning are not explosively complex.2

The idea behind the probably approximately correct (PAC) learning framework first introduced in Valiant (1984) is to require feasibility of the learning, and to achieve this we are allowed to converge to some reasonable approximation of the target hypothesis with reasonably high probability, using feasible computational resources. To formulate these requirements coherently it is necessary for the learning algorithm to select examples according to some fixed unknown probability distribution over the space of examples. Then we can define PAC-learnability of a concept class (possibly comprising functions, languages, etc.) as being fulfilled when we have a computationally tractable learning algorithm such that for each element math formula in the class and probability ∈, the algorithm converges with probability at least 1−∈ to some element math formula which disagrees with math formula only on a set of examples having total probability less than ∈ (Li & Vitányi, 2008).

Although PAC learning usually requires that the learning algorithm should run in polynomial time depending on the size of the provided example sequence, formal learnability results are often quite abstract and pay little heed to the practical fact that real human learning must be computationally feasible in some way. Alex Clark and Shalom Lappin's contribution identifies two problems, related to computational complexity, with much conventional literature on language learnability. One problem is the propensity for learnability arguments to conflate or confuse the hypothesis class with the “learnable concept” class. The hypothesis class is the class of grammars (each defining a language) that the learner can conjecture, given some input; the learnable concept class is the (potentially very much smaller) subset of languages on which the learner can identify the language in the limit. Assuming that the learner is acquiring the language from prior generations of learners who successfully learned the language in the limit, then the language to be learned must lie within this restricted set. Nonetheless, the learner need not explicitly restrict all of its conjectures to this narrower set of languages—indeed, in some cases this is provably not possible, even where the learner succeeds. This confusion can lead theorists to draw excessively pessimistic conclusions about learnability, and to overstate the importance of explicit restrictions on the hypotheses the learner conjectures. This brings us to the second problem addressed by Clark and Lappin, namely the computational complexity of the learning algorithm. In language acquisition, they argue, the key focus should be the actual method of inducing a hypothesis from input data. Consideration of the complexity constraints and requirements of such induction procedures will then suitably guide our investigations toward the best procedures for settling on a compatible hypothesis given the input, and perhaps will ultimately be more informative than would figuring out the mathematical properties of a theoretically learnable language class. Notwithstanding the focus on language learning in the article, all of the authors’ arguments apply to any cognitive learning problem that could be formalized.

Apart from the complexity of learning algorithms, the different representational complexities of concept classes—syntactic grammars/languages versus phonological grammars—is the main issue addressed in the contribution from Jeff Heinz and Bill Idsardi. The authors first establish that no phonological process in natural language is agreed to require complexity greater than is representable in a regular formal language (referring to the Chomsky hierarchy)—in fact, it is shown that the subregular classes of strictly local and strictly piecewise languages can represent phonological processes. It has, of course, long been established that recursive processes in the syntax of natural languages often require greater complexity and may even require the representational power of a context-sensitive language. The authors then argue that the best explanation for this complexity difference is that phonology is learned by a different cognitive mechanism from that applied to syntactic learning. The differences in the constraints on learning, it is argued, can best explain both what phonology allows and what it does not allow.

6. Semi-supervised learning

Most learning models about which there is any degree of theoretical study are supervised in the sense defined earlier, meaning that the learning data consist entirely of labeled examples (or are drawn from a single target concept and thus have a single default label), and the induction process essentially learns how to generalize and account for further data. The opposite approach, in which none of the examples are labeled but we expect them to be drawn from different groups or some sort of unknown structure, is known as unsupervised learning. Here, the usual goal is to extract “hidden structure” from the raw data, but without any labeling it is impossible to gauge success of the learning—we literally do not know what we are doing. An interesting combination of these two extremes is provided by the framework of semi-supervised learning, in which unlabeled examples are provided alongside the usual labeled ones in a learning task such as classification, and the learner somehow makes use of the unlabeled examples to augment the information provided by the labeled set.

In cognitive science, category learning is an area that seems especially ripe for the application of semi-supervised learning since, as stated in the current contribution from Bryan Gibson, Timothy Rogers, and Jerry Zhu, “in the real world, category learning is not fully supervised.” In their paper, one finds an overview of three standard cognitive models for category learning, which are generally supervised models: the exemplar model, in which all training examples are remembered for later use; the prototype model, in which each category is represented as a prototype having a set of parameter values; and the rational models, a family of compromises between the preceding extremes allowing the learning representations to vary from individual items (as in exemplar models) to summary representations (as in prototype models) as determined by the data. The paper also describes three common supervised models used in the purely practical enterprise of machine learning: kernel density estimation, Gaussian mixture models, and Dirichlet process mixture models; the goal is to show how semi-supervised techniques for the machine learning models can be applied to evaluate the cognitive learning models.

Modifying a supervised learning model so that it makes use of unlabeled data is called lifting the model into semi-supervised learning. Unlabeled data cannot be used directly, as they do lack labels, but they provide extra information about P(D), which is one expression for the denominator (Bayesian evidence) in the Bayes formula. When the lifting assumptions of the framework are valid, semi-supervised models, have been shown to converge more quickly than corresponding supervised models which make no use of unlabeled data. One common semi-supervised learning assumption is the mixture model assumption, which assumes all data are drawn from a mixture model. This assumption can lead to semi-supervised versions of the kernel density, Gaussian mixture, and Dirichlet process mixture models, which are in turn demonstrated by the authors to be mathematically equivalent to the three respective cognitive learning models, viz. exemplar, prototype, and the rational model proposed by Anderson (1990).

In machine learning, the motivation for lifting to the semi-supervised regime is economic; in cognitive science, the motivation is realism. Gibson et al. go on to review empirical evidence that these models describe important aspects of human category learning. Studies are reviewed which show that, not only is human category learning performance influenced by the distribution of unlabeled examples, the influence is strong enough to override the information gathered from labeled examples. The evidence from the studies does generally indicate that both the labeled and unlabeled examples are used for induction by the human subjects.

Model-fitting experiments were also conducted, in which each type of semi-supervised model was fit to empirical findings, while a single chosen parameter of each model was varied. Each model shows behavior grossly consistent with human categorization behavior under some parametrizations. To get a better quantitative picture, the empirical data were divided into training and test portions so that the models could be better evaluated on the basis of off-training set behavior—always the holy grail of model selection. Under this procedure, the semi-supervised lifting of Anderson's (1990) rational model provides the best fit with the empirical findings.

This contribution distinguishes itself from much of the cognitive science literature by providing complete algorithms for the learning methods discussed, as opposed to vague outlines. The authors admit, however, that their general conclusions are preliminary, and they suggest some useful methodological changes for future research. Their extensive survey discusses further findings about human learning strategies and also addresses some problems with extending semi-supervised learning to higher dimensional stimulus spaces.

7. Conclusion

We believe that the papers in this topic provide an excellent illustration of the power of formal methods for understanding fundamental problems of learning that lie at the heart of cognitive science, and we hope that these papers will help inspire other cognitive scientists working on learning, as well as mathematicians and computer scientists working on formal results, to focus on the exciting formal challenges raised by the cognitive science of learning. One of the most brilliant and innovative figures in this field, Partha Niyogi, was an inspiration to both the topic editors and most of the contributors. He combined dazzling technical insight with a deep understanding of the core issues in the field of machine and human learning. His untimely death in 2010 shocked the community of formal learning research; the editors and contributors would like to dedicate this entire topic to his memory.

  1. 1

    This is the subjective interpretation of probability, for which there are a variety of justifications. Probability theory also has a number of other interpretations, for example, concerning limiting frequencies of repeatable events, which do not concern us here.

  2. 2

    Typically, this is interpreted as requiring that the time and space requirements of any learning algorithm are polynomial rather than exponential or worse in the size of the set of learning examples. Even this extremely modest criterion is violated by general-purpose Bayesian methods, such as those described above.


Friday, April 17, 2015

Completed - Canopy shawl

I made another lovely shawl!  This one is for keeping my neck and shoulders snuggly and warm.
Pattern: Canopy
This shawl has three repeating sections: A and B, which look the same except are mirror image, and a stocking stitch section.  These three sections are repeated three times, with four stitches increased on each right-side row.  This makes the triangular shape!
By the end, your rows are over 200 stitches long!
This yarn, Road To China Light, is made of 65% baby alpaca, 15% silk, 10% camel and 10% cashmere.  It is so snuggly and warm and incredibly soft!  Unfortunately, as it is hand dyed, there is quite a bit of variation in each skein and you can clearly see in the photo below where I changed skeins. It's not a huge deal, but the middle skein I used had much more orangey tones.
I know you're supposed to knit a few rows of each skein to avoid this stuff, but I don't like doing that as I worry it will come undone.
Ugg boots for the win.
The border was the trickiest bit, being knit at right-angles to the shawl.  I imagine it's a bit like entrelac? Anyway this was a lovely shawl to work on and to wear.  Do recommend!


 Fair Automation

A sensor is a device that is used to detect and sense the different changes Read more

A sensor is a device that is used to detect and sense the different changes in an electrical or optical signal. Once the change in signal has been detected, the sensor usually acts by either producing an alerting sound or producing a blinking light to inform the user in cases of disruption or change in signal. Sensors are used in everyday applications as well as products.

With developed and advanced technology, more complicated sensors have been developed to accommodate the never ending need and urge to use sensors. Analogue sensors such as potentiometer have been replaced with more advanced sensors such as MARG sensors and microcontroller sensors. Moreover, the analogue sensors are still been used in the current mechanical field as their contributions are still needed and valid. However, for the analogue sensors to be used, the sensitivity has to be converted using an analogue to digital converter.

Going by their name, sensors are highly sensitive even to the slightest changes in temperature, pressure, proximity, position and vision. The sensitivity of the sensor indicates how much the output of the sensor changes when the input being measured changes. Majority of sensors are designed to have a small effect on whatever is been measured. This small effect nature allows the sensor to be more precise on the small changes been measured.

Apart from detecting changes in temperature, sensors are also used in sensing changes in pressure. Sensors under this category are usually used in pressure controlled or mediated applications. Examples of such sensors are piezoresistive, ceramic and OEM just to name a few. These pressure sensors usually act as transducers in generating a signal when certain levels of pressure are breached or achieved.

The quality of a sensor and its functions is determined by the sensitivity of the measured property or parameter, the ability to be insensitive to any property around it as well as the lack of determining the actual outcome of measured parameter. Usually, the sensitivity of the sensor is defined as the ratio between the measured property and the output signal.

Temperature sensors are sometimes regarded as the most important sensors available. This can be attributed to the fact that the slightest change in undetected temperature can lead to catastrophic results. These types of sensors usually occur in 2 types namely electrical and mechanical. Temperature sensors can be found in applications such as thermometers.

Due to their high level of sensitivity, sensors may produce quite a number of deviations or errors if the sensors used are not ideal or genuine. The following are the most common deviations.

-Sensitivity error is the most common deviation in sensors. This happens when the sensitivity differs from the value specified.

-Zero sensor error occurs if the output signal is not zero when the measured parameter is zero. Its also referred to as offset or bias.

-Non-linearity occurs when the sensitivity is not constant over the range of the sensor. It’s usually defined by the amount of the output differing from the ideal behavior of the sensor.

-Dynamic error occurs when there’s a rapid change of the measured parameter from time to time.

Sensors vary differently in terms of make, technology, design and performance. This can be attributed to the fact that many companies have sprouted with each company producing their different makes of sensors. Sensors are applied in fields such as medicine, robotics, cars, airplanes and manufacturing companies.

Read less
Pressure sensors


Hold the ice cream: Overweight moms tend to have bigger babies

Overweight or obese women give birth to larger babies, says new research that could make some pregnant women think twice before reaching for that box of ice cream.

Mothers with high blood sugar or diabetes, even within a healthy range, also tend to have bigger babies, according to a study published Tuesday in the Journal of the American Medical Association.

Conversely, having higher blood pressure during pregnancy causes women to deliver smaller babies, researchers at the British universities of Bristol and Exeter, who led the study, found.

“Being born very large or very small can carry health risks for a newborn baby, particularly when that’s at the extreme end of the spectrum,” said Rachel Freathy of the University of Exeter Medical School, who co-wrote the report.

Understanding which characteristics of mothers influence birth weights may help reduce the number of babies born too large or too small, she added.

The researchers examined data from more than 30,000 healthy women and their babies across 18 studies.

All the women had European ancestry and lived in Europe, North America or Australia. They gave birth between 1929 and 2013.

The researchers examined genetic variants associated with womens’ body mass index, blood glucose and lipid levels and blood pressure, together with measurements of those characteristics during pregnancy and babies’ birth weights.

A four-point higher body mass index (BMI) for mothers results in a 1.9 ounce (54 grams) higher birth weight for babies, the study found.

Researchers also determined ratios for correlations between higher glucose levels in mothers’ blood and higher birth weights, as well as higher blood pressure in mothers and lower birth weights.

“The estimated associations between these maternal traits and birth weight (either increased or reduced) are substantial and of clinical importance,” the authors wrote.

“They support efforts to maintain healthy gestational glucose and blood pressure levels to ensure healthy fetal growth.”

The researchers also found that lipids, or fat levels, in mothers’ blood do not appear to affect baby sizes, contrary to the findings of some previous studies.


Adenium Species in Cultivation

The genus Adenium consists of 10 or 11 distinct taxa (species and subspecies). Some are incorrectly named, and one or two new ones have yet to be described. For ease of labeling, this site assumes that each entity is a separate species, and we use the names popular among horticulturists until the taxonomic confusion has been sorted out.

Click on a thumbnail to see an image gallery of that species.

Adenium obesum (incorrect name)

Adenium multiflorum

Adenium swazicum

Adenium somalense
(may be incorrect name)

Adenium crispum

Adenium boehmianum

Adenium oleifolium

Adenium socotranum

Adenium sp. nov. (Tanzania )

Adenium arabicum (incorrect name)

Adenium arabicum? (Oman)
(incorrect name)


Northwood Tree Care

Northwood red maple (Acer rubrum "Northwood") is a mid-sized member of the maple family, reaching a height of about 50 feet and potentially living up to 150 years. Northwood trees are known for their ascending branches and brilliant red-orange fall colors. They have a rounded to oval shape, and make ideal shade trees for lawns and back yards.

Climate and Conditions

Though they grow well in a warm, Mediterranean climate, Northwood red maples are also extremely cold-hardy, making them well suited to growing in many different areas. They thrive in Sunset's Climate Zones 1 to 9 and 14 to 17, and display the best fall color in cooler areas. Northwood red maples can tolerate soils ranging from acidic to alkaline, and with textures ranging from fine to course.

Planting Location

Northwood red maples shouldn't be planted in a dry spot. They are drought-tolerant once established, but prefer moist to wet soil. They prefer full sun to partial shade, and can develop a significant width, so choose a location where they have room to grow. Plant Northwood trees away from sidewalks and other structures to avoid potential damage when the roots grow.

Once established, Northwood red maples are low-maintenance. They should be watered as needed to keep them healthy when young, but this is not necessary for healthy adult trees. Fertilizing is also not necessary, though adding mulch around the base of the trunk can help regulate temperature, hold in moisture and add nutrients to the soil. Northwood trees do not need to be pruned, although you can prune healthy adult trees if you wish to control size and shape.

Pests and Diseases

Northwood red maples are resistant to most diseases and have few pest problems. They may occasionally be susceptible to aphids, but these insects rarely cause significant damage. Generally, natural predators keep aphid populations in check. If your trees develop a serious aphid infestation, treat them with insecticidal soap, neem oil, or insecticides containing acephate, chlorpyrifos, diazinon, malathion or permethrin.

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How Atomic Clocks Work

When the power goes out and is later restored, how do you know what time to set your clocks to? Have you ever wondered how time is regulated?

In the United States, the standard of time is regulated by the US Naval Observatory's Master Clock (USNO), the official source of time for the Department of Defense. The effects of these mechanisms are felt by all of us in the form of alarm clocks, computers, answering machines and meeting schedules. In this edition of HowStuffWorks, we will learn all about atomic clocks and how they keep the world ticking!

Are Atomic Clocks Radioactive?

What Are The Types of Atomic Clocks?

Today, though there are different types of atomic clocks, the principle behind all of them remains the same. The major difference is associated with the element used and the means of detecting when the energy level changes. The various types of atomic clocks include:

How Does a Practical Cesium Atomic Clock Work?

Atoms have characteristic oscillation frequencies. Perhaps the most familiar frequency is the orange glow from the sodium in table salt if it is sprinkled on a flame. An atom will have many frequencies, some at radio wavelength, some in the visible spectrum, and some in between the two. Cesium 133 is the element most commonly chosen for atomic clocks.

To turn the cesium atomic resonance into an atomic clock, it is necessary to measure one of its transition or resonant frequencies accurately. This is normally done by locking a crystal oscillator to the principal microwave resonance of the cesium atom. This signal is in the microwave range of the radio spectrum, and just happens to be at the same sort of frequency as direct broadcast satellite signals. Engineers understand how to build equipment in this area of the spectrum in great detail.

When Was The Atomic Clock Invented?

In 1955, the National Physical Laboratory in England built the first cesium-beam clock used as a calibration source. Over the next decade, more advanced forms of the clocks were created. In 1967, the 13th General Conference on Weights and Measures defined the SI second on the basis of vibrations of the cesium atom; the world’s time keeping system no longer had an astronomical basis at that point! NBS-4, the world’s most stable cesium clock, was completed in 1968, and was used into the 1990s as part of the NIST time system.

In 1999, NIST-F1 began operation with an uncertainty of 1.7 parts in 10 to the 15th power, or accuracy to about one second in 20 million years, making it the most accurate clock ever made (a distinction shared with a similar standard in Paris).

How Is Atomic Time Measured?

The long-term accuracy achievable by modern cesium atomic clocks (the most common type) is better than one second per one million years. Hydrogen atomic clocks show a better short-term (one week) accuracy, approximately 10 times the accuracy of cesium atomic clocks. Therefore, the atomic clocks have increased the accuracy of time measurement about one million times in comparison with the measurements carried out by means of astronomical techniques.

The National Company in Massachusetts produced the first commercial atomic clocks using cesium. Today, they are produced by various manufacturers, including Hewlett Packard, Frequency Electronics, and FTS. New technology continues to improve performance. The most accurate laboratory cesium atomic clocks are thousands of times better than commercially produced units.

For more information on atomic clocks and related topics, check out the links on the next page!

Related HowStuffWorks Articles

More Great Links

About the Author

Douglas Dwyer is the founder of Frequency Precision Ltd. in the UK. He provides consulting and design services to the world-wide electronics industry. Mr. Dwyer has been involved in frequency control since the mid-1960s and has published articles on crystal oscillators, temperature-compensated crystal oscillators, oven-controlled crystal oscillators, surface acoustic wave oscillators, and industrial quartz crystal fabrication technologies. The history of clocks and watches has been an interest of his since he got involved in the design of quartz watches.


PG&E seeks to shift rates

Industries & Tags

Pacific Gas and Electric Co. has asked regulators to let it revise rates in a plan that would lower costs slightly for people who use a lot of power.

The San Francisco Chronicle reports the utility wants to reduce its rate tiers from four to three, which would help even out rates so that they are closer to actual costs of providing service.

The plan would allow people who use a lot of power to pay a little less, while people who use comparatively little would wind up paying a bit more.

Some customers have complained that the current rates favor people who live along the temperate coast, while customers in hotter inland areas, who use more power, are forced to pay more.


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Sassello (Savona), Liguria
Sassello (Savona), Liguria

Sassello is known to have been inhabited in pre-historic times by one of the Ligurian tribes; it was first mentioned in a document in 967 AD and had been passed on to many noble families like the Genoese Doria, Ponzone, Del Carretto and Bonifacio Del Vasto over the centuries. During the rule of these families several important monuments were built in the town.

The small village of Sassello (Raffaele Sergi/Flickr)

Attractions in Sassello

Bastia Soprano

The Bastia Soprano was originally built in the 12th century; however, the main fortified complex was built only in the 15th century, at the time of the rule of the Doria family. The fort was later on passed on to Genoa in 1612 who had acquired quite a lot of benefits from the iron industries located in the region and because of the proximity of the nearby coast. The fort was destroyed several times over the centuries and had to be rebuilt and refurbished a few times.

Sassello was destroyed in a fire caused by Savoias troops and was rebuilt in 1672. The present day stone houses and buildings all date back to the 17th century.

Church Della Concezione

Chiesa della Concezione, Sassello

One of the most important places to visit in Sassello is the ancient church of Concezione. The church was originally built in 1582 close to an old convent, but was extended in later centuries.

Church of San Giovanni

The church of San Giovanni is the oldest religious building in Sassello. The church dates back to medieval times, however it was severely damaged a few times during the passing of the centuries. The last renovation work was done in the 18th century and quite a lot of changes were done to the original design. The façade we see today, along with all the decorations inside, date back to the 18th century when most of the original features of the church were changed.

Getting to Sassello

Moving Around the Town

Sassello is quite a small town characterized by several narrow alleys, beautiful porticos and old stone and limestone houses. Walking around the town is the best way to explore it. There are only a few sight seeing attractions in Sassello, especially churches. These can all be visited within a day by walking around the town. Visitors who have their own cars can also drive around to visit some of the neighboring villages and explore the landscapes of the area.

Stay and Accommodation

There are only a few hotels in Sassello. Most tourists visit Sassello as a day excursion from Genoa or from other towns in the province located close by. Since there are only a few sight seeing attractions in town, most people spend a day in Sassello and return back in the evening. Some of the most well known hotels in Sassello are Pian Del Sole, hotel Monte Cucco, hotel Delfino, hotel Paradiso and hotel San Giovanni. Except for these there are a few farm houses and old villas which can be rented by visitors.

Eating in Sassello

There are a few good places in Sassello where visitors can eat out. The town has a few good pizzerias, trattorias and restaurants that serve good quality food. Visitors would mostly find the locals eating at these small restaurants and the atmosphere is usually very casual and friendly. Food is generally Italian and rustic, as preferred by the locals. Some of the most well known restaurants in town are trattoria Vittoria, ristorante Paradiso, ristorante Piccolo Borgo, Locanda il Fungo and La Donda.

Shopping in Sassello

Chiesa della Santissima Trinità, Sassello

There are a few good sh

ops in Sassello where visitors can purchase local products. The older section of the town is lined with small shops selling local food products, fresh fruits, vegetables, clothes and handicrafts. However, the best things to purchase in Sassello are locally made cheeses, good quality extra virgin olive oil, salami and honey. Most wine shops in Sassello also have good quality local wines that are made from the grapes grown in the region. Sassello is particularly known for its "amaretti," a soft almond cookie typical of Liguria and southern Piemonte.

What do you think ?


Nourishment Challenge: Day 9

Days 5 – 8: Silence. Watching. Listening. When I’m reminded that the world is on fire in a way that touches my particular compassion cord, I encourage myself to be quiet. And then I encourage myself to grieve in a way that honors loss of life and the often subsequent othering of the perpetrator. What does my grief look like? Screaming? Crying? The biblical gnashing of teeth? If I can take the time to give myself over completely to grief for a short time, I can connect to the beings that are suffering. I can be present.

Day 9: Black. People. Because Obama popped off, Patti’s pies, and Missy.

I am deeply nourished by the fierceness of my people’s urge to laugh, to be fully expressed, to celebrate life in the face of hurricanes. When the President of the United States uses the phrase “if folks wanna pop off” in reference to ISIS strategy, when a brotha transforms into Patti Labelle while eating her pies in a video review, when my girl Missy Elliot casually drops FIRE after years of not being on the scene…we are truly magnificent. Breathing in the rawness and sass and funk and soul and swagger and love of black people gives me life.


Taco Soup

1 lb. ground sirloin
1 lb. ground chicken or turkey breast
6 Tbsp. chili powder
3 Tbsp. ground cumin
1½ tsp. garlic powder
1½ tsp. onion powder
½ tsp. sea salt
4½ tsp. smoked paprika
3 tsp. ground coriander
1½ tsp. cayenne pepper
2 tsp. cornstarch
2 c. low-sodium beef broth
1 (2.87 oz.) container chili powder
2 qt. tomato juice
1 (15 oz.) can tomato sauce
2 (14.5 oz.) cans diced tomatoes
2 (15 oz.) cans black beans, drained
Fresh tomatoes, diced
Green onion, sliced
Lettuce, shredded
Black olives, sliced
Shredded cheddar cheese
Guacamole or diced avocado
Sour cream
Tortilla chips or thin tortilla strips

Combine the chili powder, cumin, garlic powder, onion powder, salt, paprika, coriander, and cayenne pepper in a bowl; divide the mixture into a ⅓ portion and a ⅔ portion, mixing the cornstarch with the ⅓ portion, and set aside.
Spray a stockpot with cooking spray and add the ground sirloin and chicken or turkey breast, and salt and pepper to taste. Cook until all of the meat is cooked through and slightly browned, about 3 to 4 minutes, stirring occasionally, to break up the meat.
Once the hamburger is cooked through, add the ⅓ portion of spice mixture and beef broth; bring to a simmer and cook, uncovered, until sauce is slightly thickened, 2 to 3 minutes. Mix the remaining ⅔ portion of spice mixture and the chili powder with some tomato juice and whisk until well incorporated, and then add it to the stockpot. Add the remaining tomato juice, tomato sauce, canned diced tomatoes, and black beans. Simmer the soup for at least an hour to allow all of the flavors to marry and enhance.
When soup is ready, serve it in bowls (make sure to leave some room at the top of your bowl) and top with fresh tomatoes, green onions, lettuce, black olives, shredded cheddar cheese, avocado, and sour cream. Sprinkle with thin-cut tortilla strips or serve with tortilla chips.


Delicious Mapo Tofu

Delicious Mapo Tofu

From small children to adults, everyone can enjoy this dish! Try this easy mapo tofu dish at home!

Ingredients: 2-3 servings

1 block (400g )
Ground pork
50 g
Japanese leek (finely chopped)
1/3 leek
Garlic (finely chopped)
1 clove
Ginger (finely chopped)
1 thumb length's worth
☆ Sugar
2 teaspoons
☆ Doubanjiang or Miso
1 teaspoon and increase to your preference
☆ Sake
1 tablespoon
☆ Soy sauce
2 tablespoons
☆ Water
120 ml
Katakuriko slurry
As needed
Sesame oil
1 tablespoon
as much as you like
Green onion (for garnishing)
A little

1. Cut the tofu into 1 cm cubes. If using silken tofu, lightly blanch (to prevent the tofu from crumbling).
2. Finely chop the Japanese leek, garlic and ginger.
3. Heat a wok, add the sesame oil and fry the ingredients from Step 2 (add the garlic while the temperature is low). Then add the ground pork and fry on high heat.
4. Add the ☆ ingredients into a bowl and mix. Add the contents of the bowl into the wok and simmer.
5. Next add the tofu and mix well.
6. Let it simmer and then add the katakuriko slurry. Sprinkle a small amount of chopped green onion on top and you're done!

Story Behind this Recipe

This is a routine dish in our family as we try to avoid using artificial seasoning. I wanted to deepen the flavor so I modified the seasonings and settled on this recipe.


questions & answers: itinerary check

is a jr pass worth it?

Is a JR pass worth it?

This is my preliminary itinerary for next April.

Day 1-3: Tokyo
Day 4: Tokyo --> Nikko --> Tokyo (Day trip)
Day 5: Tokyo --> Osaka
Day 6-7: Osaka
Day 8: Osaka --> Kyoto
Day 9-11: Kyoto (maybe day trip to Nagoya one day)
Day 12: Kyoto --> Takayama
Day 13-14: Takayama (one day trip to skirakawa)
Day 15: Takayama --> Tokyo
Day 16: Tokyo --> Hakone --> Tokyo (day trip)
Day 17-20: Tokyo

I am thinking to get a 14 day JR pass that starts on Day 4 and ends on Day 18? Is it worth it? Do i need to pay an extra reserve seat fee and reservation fee for the trips between these days?

Or should i get a 21 day JR pass?

Thanks in advance.

Hi there,

If you use a 14 day JR Pass from day 4, then the last day of use is day 17. That said, I do think a 14 day JR Pass would be the best option for your itinerary. As it covers all your rail travel between the cities you plan to visit and you can make good savings compared to purchasing normal JR tickets.

The JR Pass includes free seat reservations.

Hope this helps,

Expert button 2

Which of these trips do I require to make a seat reservation?
I will also be travelling with a 3 year old, does she get a seat if she is free?

Hi there,

I would make seat reservations for the following routes:

Tokyo --> Nikko --> Tokyo
Tokyo --> Osaka
Kyoto --> Takayama
Takayama --> Tokyo

For children see:

Expert button 2

Order your Japan Rail Pass Online

Starting from $259.00 for 7 days of unlimited Japan Rail travel


Junbo Sun

Learn More
This paper develops a discrete operation optimization model for combined heat and powers (CHPs) in deregulated energy markets to maximize owners' profits, where energy price forecasting is included. First, a single input and multi-output (SIMO) model for typical CHPs is established, considering the varying ratio between heat and electricity outputs at(More)
High reliability is the key to performance of electrical control equipment. PLC combines computer technology, automatic control technology and communication technology and becomes widely used for automation of industrial processes. Some requirements of complex PLC systems cannot be satisfied by the traditional verification methods. In this paper, an(More)
This study investigated the use of a newly developed chitosan-Ca pectinate microbead formulation for the colon-targeted delivery of anti-A/B toxin immunoglobulin of egg yolk (IgY) to inhibit toxin binding to colon mucosa cells. The effect of the three components (pectinate, calcium chloride, and chitosan) used for the microbead production was examined with(More)
Network-connected combined heat and powers (CHPs), owned by a community, can export surplus heat and electricity to corresponding heat and electric networks after community loads are satisfied. This paper proposes a new optimization model for network-connected CHP operation. Both CHPs' overall efficiency and heat to electricity ratio (HTER) are assumed to(More)
The number of wind farms is increasing worldwide and capacity of variable-speed wind turbines has been increased significantly. This has raised wide concern over the power quality, especially harmonic current emissions of variable speed wind turbines. However, the current IEC standard does not have corresponding coping methods, including measurement and(More)
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Categoria: File e comportamenti sospetti
Tipo: Suspicious behavior

Runtime behavior alerts of this type inform the user that a suspicious-looking program has attempted to modify the DNS settings of the computer. Any attempt at this behavior by an unauthorized program could indicate a malware infection.

HIPS/RegMod-017 uses Sophos Behavioral Genotype technology to enhance detection accuracy.

scarica Prova gratuita dei prodotti Sophos
Scarica subito


What is encryption?

Sometimes, information has to be masked in a predefined way so as to shield it from unwarranted perusal by parties other than the intended recipient. In computing data encryption has become a major requirement especially among corporate bodies. Data encryption is the process of altering electronic data into a state that is unreadable to the human eye. Usually, a set of ciphers or algorithms are used to come up with it. In the traditional concept, data encryption was mostly used by governments and military agencies to transmit hypersensitive electronic data. But now data encryption has become a fundamental day – to – day undertaking for the public as well. Nowadays it is a requirement for web browsers to encrypt data by default as they connect to secure servers.

What is encryption

How encryption is used

Governments were the sole users of encryption algorithms until “Diffie-Hellman key exchange” and “RSA algorithms” came on to the scene. The broader use of encryption was initiated by the two entities above led to the widespread use of encryption in the public domain, especially in the commercial and consumer realms. They cover all electronic data while both in transit (send/receive) mode or storage (e.g. hard disk, USB, smartphones, etc.) mode. Whether the information is hacked while in storage or waylaid while in transit, the perpetrators cannot decipher whatever is on it.

Encryption may not be a huge factor on the individual computer user (especially for personal use). But for businesses it is a major priority. Most security breaches in companies stem from unauthorized access on confidential business information. The costs resulting from such breaches can be anything up to millions of dollars annually. Basically, encryption of data involves scrambling its content into a jumbled format which cannot be accessed by unauthorized parties.

Data Encryption Methods

a) Triple DES

This is the first instance of data encryption that came in to replace the customary Data Encryption Standard due to recurring breaches. Hackers had worked out methods to decode the DES encryption and it was no longer deemed to be robust. It went on to become a headliner in the security based systems development. It utilizes 3 distinct keys which are 56 bits in length. It slowly phased out with quick advancements in technology but is still very much a reference standard.

b) RSA

This is the most widely accepted standard for data encryption over the internet. It is asymmetric in nature because it uses a pair of keys; the public key and private key. It is very efficient because it takes a lot of effort for hackers to work through it. That is why asymmetric encryption is a favorite among SMEs

c) Blowfish

This one was also designed to take over from DES. It is symmetric. It splits data into a pair of 64 bit chunks and then proceeds to individually encrypt them. Its admired features include high speed and effectiveness. It is almost un-hackable. Better yet, it is free to the public. It is most popular for ecommerce sites and online payment platforms. Symmetric encryption cannot be breached easily; and even then, the hackers would need additional cypher tools to make sense of the jumbles.

d) Twofish

Twofish was created by the same person who made Blowfish. Howver only one key is required for Twofish. Naturally faster than Blowfish, Twofish is popular in both software and hardware application.

e) AES

AES stands for Advanced Encryption Standard. It is widely used by the U.S Government as well as other high profile organizations. To date it is regarded as the strongest encryption. It can make use of both 192 bits and 256 bits apart from the basic 128-bit standard.


NSCAD Graduation Catalogue

Hildur M H Jónasson

Visual Arts Certificate in Studio: Printmaking

The ghost
In The ghost, I investigate history, folklore, mythology, and environmental issues of my birthplace, Iceland. The disappearing landscape of melting glaciers takes global warming to a personal level as I mourn the loss of a land that makes up the cultural fabric of a place where my roots lie. It is a reminder of impermanence and fleeting time.

Black Island
This is a typical island from my place of birth, Iceland. I was remembering a body of islands from Breiðafjörður, a fjord I’ve visited many times. This black volcanic rock island with it’s shimmering reflection on the fjord stood like an imposing Richard Serra sculpture floating out on the sea. Erik the Red started his voyage here to establish a Nordic settlement in Greenland. A few years later, around 1000 AD, his son Leif Erikson became the first European to step foot on North American soil.

Snowball is an abstracted photo-based image of a particular tea light holder that can be found throughout many Scandinavian households. This art glass celebrates winter and emits a magical light that is refracted through the textured glass. By composing and abstracting the image towards a “scape”, I am strongly reminded of winter celebrations in Iceland where overly large bonfires burn on New Years’ Eve and the hidden people are celebrated well into the night.


Bellydance Terminologies Illustrated E-Book`

A comprehensive guide to cultural, colloquial and general bellydance terminologies

Bellydance enthusiasts enjoy a language that often transcends borders - from the native Middle eastern dance and music terminologies to the 'coined phrases' that have emerged as dancers worldwide have communicated their passion for the art and created terminology that has become known internationally.

This guide is a useful resource for teachers and students - with terms that describe the music, culture, styles, genres, instruments, rhythms, qualities, costuming, movements and historical figures of the bellydance or Raks Sharqi world.

Folkloric terms, and tribal elements are also covered - the approach is a broad spectrum of dance terminologies that describe the many styles that have evolved world wide. There are many Middle Eastern words, as well as the
Western modifications and word-creations that have been developed and widely used by bellydancers, to highlight the newer realms of contemporary and fusion bellydance.

Each page features illustrations to highlight several key terminologies.

Keti Sharif
jam-packed pages just $4!


Cover crops are reportedly good for many purposes. They can reduce erosion, fix nitrogen, add organic matter, breakup hardpans, capture soil nitrates, feed soil microbes, control weeds, and improve water infiltration. They might remove excess moisture, keep soils cool, or reduce evaporation. Hopefully, they maintain or improve our stewardship of the soil resource and result in improved cropping system profits.

Planting cover crops such as brassicas and small grains during late summer or early fall is becoming a more common and accepted practice. Often these cover crops are grazed as a forage cover crop. These species have been used as high quality forage for many years.

However, both the brassicas and small grains can accumulate high levels of nitrates. While the specific level at which nitrates become risky to cattle is unknown, most recommendations consider diets less than 3,000 to 5,000 ppm nitrates (NO3) as safe for all livestock, over 9,000 to 10,000 ppm nitrates as potentially toxic, and levels in between as risky for pregnant cattle. Note that these levels are reported as “nitrates”. Some labs report ‘nitrate nitrogen”. Nitrate nitrogen is nearly 4 times as risky as straight nitrate.

Nitrate concentrations in excess of 20,000 ppm nitrates have been reported for these crops in recent years, especially for brassicas like turnips. Thus, many fields containing these forage cover crops have appeared dangerous to use. Factors like high levels of soil nitrate, mineral imbalances, and growing conditions that slow plant growth like drought, frost, and cloudy weather can increase nitrate accumulation but these factors did not seem to fully explain the high levels measured in these cover crops.

Because of these high nitrate levels, many fields with large amounts of high quality, but potentially toxic, forage cover crops were left unused. This certainly was a reasonable decision given the potential loss of life and financial loss that may have resulted from using the high nitrate forage.

Some producers, though, did graze their forage cover crops despite the apparently high risk of nitrate toxicity. Since there have been very few reports of losses due to nitrates from using these forages, it is obvious that much remains unknown regarding toxic levels of nitrates and using high-nitrate containing forages. In particular, high quality forages with elevated nitrate levels might be grazed with less risk than currently thought. However, what might be the safe level of nitrates remains unknown.

Brassicas and small grains should continue to be considered for use as forage cover crops. Samples of the forage should be collected periodically, though, and tested for nitrate content. If high levels are detected, the safest alternative is to allow these high nitrate crops to remain as traditional cover crops until the next cropping season for soil and moisture conservation. Much of the N in the nitrates will remain for subsequent cropping systems.

If still used as forage, take appropriate steps to minimize losses. Risk of nitrate poisoning can be mitigated several ways. Nitrate poisoning is dose-dependent -- both the amount and concentration of nitrate in the diet as well as how rapidly it’s consumed will influence the likelihood of toxicity. So avoid rapid consumption of high nitrate forages. Grazing and feeding strategies that either reduce the amount of nitrate consumed or spread it’s consumption across a longer time period will lower the chance of animals being harmed. These strategies may include any of the following:

- Begin grazing with a small number of less valuable animals that are observed carefully for signs of distress from nitrates.
- Adapt animals slowly to high nitrate forages.
- Graze pastures lightly to allow animals to selectively graze plant parts that are lower in nitrate concentration.
- Avoid feeding high nitrate forages to very hungry animals.
- Feed a probiotic that provides the rumen with microbes that convert nitrates to a non-toxic form.
- Dilute nitrate concentration by also feeding a low nitrate containing feedstuff.
- Feed grain to supply energy for rumen microbes to convert nitrate into bacterial protein.
- Feed frequent, small meals to spread out nitrate consumption.
- Ensiling forage often, but not always, reduces nitrate concentration 40 to 60 percent.
- Use extra care feeding high nitrate forage that has been ground due to rapid consumption and reduced selectivity.

If there are any concerns about the potential for nitrate toxicity, a reliable forage test will provide an indication of the management strategies needed. Even though the potential for nitrate toxicity exists, careful management can be used to reduce livestock losses.


The Politics of Teacher Evaluation Formulas

As states revamp their teacher evaluation systems, they continue to search for that magic number: the percentage of a teacher evaluation rating that should be based on student academic performance. Here’s how this has played out over the past month:

  • The Ohio State Legislature voted to lower the weighting for student growth from 50 percent of a teacher’s evaluation to 42.5 percent. Why the seemingly random choice of 42.5 percent? Because the state Senate wanted it revised downward to 35 percent and the House wanted to keep the weighting at 50 percent. Legislators compromised on 42.5 because it lies smack dab in the middle of 35 and 50.
  • In New Jersey, Governor Chris Christie signed an executive order mandating that statewide exams account for 10 percent of a teacher’s evaluation this upcoming school year rather than the previously decided upon 30 percent. It will climb to 20 percent in 2015-16.

The issue here isn’t whether 10 or 35 or 50 percent is the right amount of student growth in teacher evaluations. No one knows for sure what that number is, and no one knew it when states set their initial student growth weightings either.

Ironically, we have better evidence now than we did when states made their initial decisions. The 2013 MET Project report found that weighting student growth between 33 and 50 percent of a teacher’s evaluation score would provide the best combination of predictive power and year-to-year stability. The MET Project is by no means definitive and we could certainly use more research in this realm. But before seeing any results or carrying out their own analyses, states are pre-emptively lowering their student growth weighting. And instead of using the evidence that does exist, states are allowing political battles to drive their decisions.

–Chad Aldeman and Carolyn Chuong

5 thoughts on “The Politics of Teacher Evaluation Formulas

  1. Ajay Srikanth

    This says nothing about the politics of the actual growth model selection for teacher evaluation. Many states are using Student Growth Percentiles, which are not valid for use in teacher evaluation as are often biased against schools w higher percentages of impoverished children. See here:

    Also, details such as measurement error and the appeals process are crucial!

  2. Linda/RetiredTeacher

    Here is something simple for everyone to consider:

    In order to evaluate a teacher based on her students’ test scores, you would have to have a test that is designed to measure each child’s school learning as opposed to his learning in general. At this time there is no whole group test that can do that. If a standardized test is given as directed (no peeking) the scores generally reflect the socioeconomic background of the student. This applies to almost all standardized tests, including the SAT.

    Of course, the in-school progress of a child can be measured, but at this time such testing requires the involvement of skilled professionals who would give pretests to each child in the fall and then follow that child’s progress throughout the year. At this time, there is no two-dollar group test that can assess the in-school learning of each child while evaluating the teacher at the same time. Do people really not know this, or are they pretending not to know it?

    In excellent schools throughout the country, administrators make it their business to be aware of the classroom progress of every child in the school. It should be obvious to everyone that a teacher cannot be evaluated according to the progress of her students if no one is aware of that progress!

  3. Britani

    I believe that Linda is correct. How can a standardized test accurately measure student learning and growth. On testing days, my students are not themselves. They are nervous and worried about this “special” test that they have to take. I have found that my students demonstrate over and over again their learning within the classroom. However, this is not translated over to the standardized tests. I think that student progress should not determine a teachers evaluation. Students range in ability is great within a classroom. Each year, I get new students, who were nothing like my previous class. They are so different in many ways. This provides an unfair way to evaluate teachers. If we are going to use testing as a way to evaluate a teacher, then we need to create a test that accurately measures a child’s knowledge.

  4. Jasmine Brown

    I disagree that standardized tests provide an accurate measure of student growth. I do agree that teachers should be held responsible for their performances. There should be alternative methods that parallel student learning with teacher evaluations. Students have various learning styles and many other factors that can alter their performances on standardized tests

  5. Lisa

    Britani, It is interesting that you posted how your students are not themselves on testing days. In order for these test to truly be standardized, each child would be taking it in the exact same environment at the exact same time and given the exact same instructions in the exact same way. Our building has always had issues with temperature. In some rooms it may be extremely hot while others may be freezing. This is not a standard environment for testing. How then can we say that these test are an accurate measurement of students learning/growth and then use this information for or against a teacher?

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Buddhist Meditation

May 29, 2009 by
Filed under Types Of Meditation

Buddhist MeditationIt goes without saying just how important meditation is to the Buddhist religion. To state the obvious meditation  is the core of the Buddhist practice. Buddhist meditations have a variety of techniques derived from ancient Buddhist texts. Buddhist meditation has evolved over a span of many thousands of years. These techniques helps to develop some great virtues of the human mind, namely mindfulness, concentration, tranquility and insight.

The benefit derived from these techniques is not only confined to Buddhists. Buddhists consider meditation as a path towards attainment of Enlightenment and Nirvana. Non-Buddhists pursue these techniques for purely mental and physical well-being and non-religious spiritual aims. Even medical science has adopted Buddhist meditation techniques to deal with the various aberrations of human mind.

These Buddhism meditation techniques have been adopted all over the world including the west for health and spiritual reasons. One reason for such a wide acceptance is these techniques do not have any authoritative doctrines.

Among various Buddhist traditions, Theravada tradition contributes to over ninety methods in developing mindfulness meditation and concentration collectively. Likely, the Tibetan tradition also has abundant meditation techniques which have elements of visionary qualities.

According to the Pali Canon School, Buddhist meditation can be practiced to develop right mindfulness, right concentration, and right view.

There are various inferences or suttas. Among them in the Sattipathana sutta, Buddha has framed four ways of attaining mindfulness. They are the body, feelings, state of the mind, and mental objects. He has further divided them into different parts:

Body (kaya)

  1. Breathing
  2. Postures
  3. Clear comprehending
  4. Reflections of repulsiveness of the body
  5. Reflections on material elements
  6. Contemplations regarding to the cemetery

Feelings (vedana)

Mind (citta)

Mental content(dhamma)

  1. The hindrances
  2. The aggregates
  3. The sense-bases
  4. The factors of enlightenment
  5. The four noble truths.

Buddha has framed two qualities of the mind, which is attained through meditation, namely serenity and insight. Collectively they are called the swift messengers of Nibbana.

Buddha has preached forty methods of meditation which will develop concentration and consciousness. Moreover, he has advised to choose the most suitable path among the forty methods, which align with ones mental makeup. One should seek a knowledgeable individual for guidance.

Buddhist meditation techniques have also incorporated practices from others like Zongmi Typology, which has been utilized by the Chinese and others. According to this, all meditation techniques appear to be the same externally. But they differ on a wide basis internally in the motive and purpose. They have five categories of meditative techniques: ordinary, outside way, small vehicle, great vehicle and supreme vehicle.

According to a meditation teacher Kamalashila of western Buddhist order, there are five basic methods of meditation:

1. Mindfulness of breathing

2. Metta bhavana including all four Brahma-viharas

3. Contemplation of impermanence including contemplation of decomposing corpse, reflection on death, reflection of Tibetan book of the dead’s “Root Verses”, Contemplation of mental states and external objects

4. Six element practice

5. Contemplation of conditionality.

To make sense of all this, Buddhist meditation techniques are well suited for anyone interested in advancing their meditation practice. You may practice them for a number of reasons, such as general well-being and spiritual advancement.

What Is Mindfulness Meditation?

May 29, 2009 by
Filed under Types Of Meditation

Mindfulness MeditationMeditation is the path to self realization and tranquility. One of the most important meditation techniques is the mindfulness meditation.

Mindfulness is a mental state that enables you to stay with a calm mind. It helps you stay aware of all the physical and mental activities of the present.  It is a form of Buddhist meditation that has been practiced for eons. According to Buddha’s noble eight fold path, it is the seventh element. Mindfulness develops wisdom of the human mind.

Insight is another name for mindfulness meditation. This type of meditation helps to concentrate on the present. This technique allows you to let go of  the past and future thoughts, hindering a certain peace of mind. During mindfulness meditation, you stay aware of the mind’s thoughts that are going on, and any actions that are happening in the present moment. This meditation technique helps to focus on the real present and develop concentration.

To really understand the meaning of and the benefits of mindfulness meditation you will need to practice it. The basic preparation and guidelines to practice this meditation technique properly is discussed below:

Create a favorable environment. This is one of the most important things to do.  Mindfulness for beginners is quite easy, if you take that all important step and get started. There should not be any sort of distraction in the place that you will select for practicing this meditation technique. This place should be calm and quiet. There should not be any sound or noise to distract you and disturb you during your meditation. This place should be such where you can forget all your pain, sorrow and can get rid of all your stress. This place should emit a sacred feeling. It can be a religious place or any place of your choice which gives you positive vibes. You don’t need to be a practicing Buddhist to see the benefits that come from a mindfulness practice.

Starting your meditation is a rather simple process. It may help to start meditating for just short periods, but do so frequently. Do not sit for long periods of meditation initially. You’ll need to slowly adjust to the meditation techniques for best results. Utilizing a meditation timer is a great way to keep track of time without worrying about a clock.

Having the correct posture is crucial step in mindfulness for beginners. There is a strong connection between the body and the mind. Hence, to control your mind you need to control your physical posture. The right way of meditating is to have an upright erect posture. Think of the traditional cross legged (lotus) pose. Keeping an erect posture helps with concentration, though you should not experience any discomfort. When it comes to meditation for beginners staying focused is one of the biggest hurdles.

Your focus should be down around the few inches around your nose. The eyes can remain open and still not be distracted by your  surroundings.

The goal here is to focus on your breath during meditation. Your natural breathing process should be the object of concentration during your meditation. You may focus on your exhale as it passes through the back of your nasal cavity or you can feel the air as it escapes past your nostrils. If you get distracted, that’s okay, just refocus and feel your breath as you come back to the meditation.

Remove your thoughts and focus on the present through your breathing. It may be easier said than done, but this is what we strive for. Initially it will be very difficult to focus and concentrate your mind. You will feel yourself getting distracted by the constant chatter of the mind. By feeling your breath to the core, you can gradually root your mind in the present and stay focused.

These are the very basic fundamentals, as they relate to practicing mindfulness meditation. There are many Buddhist texts and guided meditations that will certainly help advance your practice.


WHAT WE LIKE: launching a weekly section!

...videos, articles, projects, drawings, books, exhibitions and everything else we LIKE! Every Friday is WHAT WE LIKE!

Boris Tellegen aka DELTA: “A FRIENDLY TAKEOVER” from 03/02 to 28/05

“Beautiful Losers”

Inspirational documentary to watch here!

Mima - Boris Tellegen

Boris Tellegen

"My work is in collage, sculpture, drawings, videos and installations. It investigates the tension entre schedule and happenstance; the semblance of order schematic undone by chaos. Mankind is driven by an unshakeable faith in progress, Where modern technology and constant growth leads us to a better world. My work explored a different outcome, Where the force unleashed automated Develop structures and minds of Their Own."

read more


Participatory Ecosystem

A participatory ecosystem is a business ecosystem with relatively low barriers to economic participation, artistic and professional expression, and civic engagement by all stakeholders, including producers and consumers.

It has strong support for creating, sharing and increasing the production of goods and services of value to the ecosystem. A participatory ecosystem will have system processes where established stakeholders are incentivized to share knowledge, access to markets, and capital to new stakeholders in the ecosystem, and to turn consumers into producers.

Stakeholders with leadership positions in a participatory ecosystem may change over time, but the function of an ecosystem leader is valued by the community because it enables members to move toward shared visions, grow the market, to align their investments, and to find mutually supportive roles in the ecosystem.

A participatory system is resilient, and has system processes, such as decentralization, to prevent bad actors. Every stakeholder in a participatory ecosystem believes that their goods and services matter, and feel some degree of social connection and community with one another (at least they care what other people think about what they produce.) Not every stakeholder must produce, but all must believe that they are free to produce when ready and what they contribute will be appropriately valued.

(source: Created by Christopher Allen, a mashup of Henry Jenkins’ “participatory culture” and James Moore’s “business ecosystem” definitions)


The Actopolis Project

The international project Actopolis wants to change cities with artistic ideas.

Actopolis is a project of Urbane Künste Ruhr and the Goethe-Institut that deals with the future of urban life. Sixty-five curators, artists and architects will develop over several years ideas for Ankara, Athens, Belgrade, Bucharest, Mardin, Oberhausen, Sarajevo and Zagreb. A short interview with Katja Aßmann, Artistic Director of Urbane Künste Ruhr.

Actopolis is about getting “the arts to act”. How can art and culture help shape the development of cities in specific terms?

Art and culture can’t change cities from one day to the next, but they can contribute to changes or at least stimulate them. Art and culture can provide food for thought, open up new possibilities and above all motivate citizens to help actively shape their city. The advantage is that art works differently from classical city planning. Art can experiment, explore boundaries. It can also playfully address and integrate people in a surprising way.

What connects the participating cities?

The cities are of course culturally very different and each has its own structural problems. But some things connect them – among others, the high unemployment, and some of the Eastern European Actopolis cities lie on refugee routes. The cities deal with their problems in very different ways. The artistic strategies of the invited artists are also very different. Some treat their subjects in a more discursive manner, others in an activist way, and still others, like the “geheimagentur” in Oberhausen, playfully.

What exactly is happening in Oberhausen?

In Oberhausen we’ve written the motto “We’re building a new city” on our banners. For instance, where before there was only a pedestrian zone, now there’s an allotment garden. On a disused railway track you can hear “spirits of good hope”. If you would like to explore the new city, you can take part in guided tours beginning in the city centre. Here too the first chewing gum museum in Europe will open its doors. Finally, this year we’re setting up so-called micro-residences in Actopolis cities. In this way artists can work in other cities for a relatively short space of time.


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379 New local job ALERTs in the last 30 Days
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A Success Story takes a work achievement and transforms it into a short, structured “explanation” that is easy to understand.

A success story (see samples here) is a powerful tool to help you in interviews because it organizes your thoughts and responses into a few sentences, creating a “story” of the correct length; keeping you on topic. By practicing a success story, out loud, you will gain confidence. When you have a success story prepared, the key achievements of your career will be showcased with less effort and worry.

Create a Success Story:

STEP ONE: describe… What was the problem you encountered? and… What were the risks or costs of doing nothing? ONE or TWO sentences.

STEP TWO: describe… What you did in ONE sentence.

STEP THREE: describe…The results and the proof that what you did was a success. ONE or TWO sentences.

STEP FOUR: be prepared with:

  1. an explanation in greater detail about STEP ONE
  2. more details for what you did in STEP TWO
  3. further explanations related to STEP THREE

more on stories & why they are so important

some narrative theorists that there is a universal story structure. These scholars claim every engaging story has this structure, called the dramatic arc. It starts with something new and surprising, and increases tension with difficulties that the characters must overcome, often because of some failure or crisis in their past, and then leads to a climax where the characters must look deep inside themselves to overcome the looming crisis, and once this transformation occurs, the story resolves itself.


soldat: Solubility Data

Description Usage Format Examples

A data set that contains information about compounds used in drug discovery. Specifically, this data set consists of 5631 compounds on which an in-house solubility screen (ability of a compound to dissolve in a water/solvent mixture) was performed.

Based on this screen, compounds were categorized as either insoluble (n=3493) or soluble (n=2138). Then, for each compound, 72 continuous, noisy structural descriptors were computed.

A data frame with 5631 observations on the following 73 variables. Some rows have missing data.

Search within the ada package
Search all R packages, documentation and source code

Please suggest features or report bugs with the GitHub issue tracker.


Sister Cities of the World

Sister cities of Denver

With a 2012 estimated population of 634,265, Denver ranks as the 23rd most populous U.S. city. The 10-county Denver-Aurora-Broomfield, CO Metropolitan Statistical Area had an estimated 2011 population of 2,599,504 and ranked as the 21st most populous U.S. metropolitan statistical area. The 12-county Denver-Aurora-Boulder Combined Statistical Area had an estimated 2011 population of 3,157,520, which ranks as the 16th most populous U.S. metropolitan area. Denver is the most populous city of the Front Range Urban Corridor, an oblong urban region stretching across three states with population of 5,467,633 in 2010. Denver is the most populous city within a 500-mile (800 km) radius and the 3rd most populous city in the Mountain West and the Southwestern United States after Phoenix, Arizona and El Paso, Texas.

Content on this page is licensed under CC-BY-SA from the authors of the following Wikipedia pages: Lists of twin towns and sister cities, Denver. Note that the data on Wikipedia is highly unreliable. In many cases, sister cities are missing or wrongly listed. Some cities also have different levels of partnership. If you find an error, please make a correction on the relevant Wikipedia pages and cite your sources.


Journal Article Prediction with Model-Based Neutrality

FUKUCHI, Kazuto  ,  KAMISHIMA, Toshihiro  ,  SAKUMA, Jun

E98.D ( 8 )  , pp.1503 - 1516 , 2015-08 , 電子情報通信学会
With recent developments in machine learning technology, the predictions by systems incorporating machine learning can now have a significant impact on the lives and activities of individuals. In some cases, predictions made by machine learning can result unexpectedly in unfair treatments to individuals. For example, if the results are highly dependent on personal attributes, such as gender or ethnicity, hiring decisions might be discriminatory. This paper investigates the neutralization of a probabilistic model with respect to another probabilistic model, referred to as a viewpoint. We present a novel definition of neutrality for probabilistic models, η-neutrality, and introduce a systematic method that uses the maximum likelihood estimation to enforce the neutrality of a prediction model. Our method can be applied to various machine learning algorithms, as demonstrated by η-neutral logistic regression and η-neutral linear regression.

Number of accesses :

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Iowa getting warmer, wetter
February 17, 2017

To the editor:

It is the rate of climate change (faster than expected by scientists) that should concern farmers, though Jerry Crew's no tillage and adopting cover crops are part of the necessary adaptation (Feb. 3 letter to then editor).

Climate deniers argue that "some of the worst weather has happened in the distant past." I use standard weather data from Iowa's climatologist for 144 years to show extremes clustered after 2006.

For 2016, Iowa's Total Average Precipitation (TAP) of 38.97 inches was recorded as the 19th wettest. However, it was less than Iowa's TAP from 2010, 2008, 2007, 2015, 2009 and 2014.

Seven of the 19 wettest years for all of Iowa were in the last 10 years, only 12 were from the prior 124 years. If precipitation amounts were random as Crew seems to suggest, such a wet year would only happen every seven years or so.

Iowa's actual and historical Annual Average Temperature (AAT) for all of 2003 was 47.8 degrees F. By 2016, the actual AAT was 50.6 degrees F., and the historical AAT was 48.1 degrees F. Nine of the interim 13 years had been hotter than 2003's AAT. (2012 was the 4th warmest year in 140 years; 2016 the 5th warmest; and 2006 the 14th warmest.) Lately, Iowa is getting wetter and warmer.

The rate of climate change could become even faster or more varied. The half life of CO 2 in the atmosphere is around 100 years causing total accumulations to increase even when annual emissions decrease. Hot air holds more water vapor than cold air. Feedback loops from melting permafrost or dark oceans replacing glaciers amplify impacts, as may other factors.

Farmers should be asking how best to reduce their carbon footprints to slow down climate change. Jerry Crew recognizes that no-till and cover crops create carbon sinks, keeping CO 2 in the ground.

I hope climate change scientists can discover more ways to prevent, or at least slow down, extreme weather disasters so our planet can recover.

Liz Gilbert

Iowa Falls

Regular Size Farm News


ES0170L Bloch - Bloch

Women order 2-3 sizes smaller than street size
Introducing the revolutionary pointe shoe, the B Morph. Developed by Bloch in conjunction with elite dancers from around the world, the B Morph is the fastest pointe shoe in the world to be broken in. The B Morph is moulded to perfection by the individual dancer by applying direct heat from a hair dryer to the parts of the shoe that the dancer wants to adjust. The special D Paste becomes pliable once heated, allowing the dancer to work the shoes to their liking by bending with their hands or walking gently around the studio. Once satisfied the dancer simply places the moulded shoes into their fridge over night and the paste will set hard like a new pair of shoes, ready for use, tailored to the individual dancers needs. The shoes can also be placed in the fridge after any heavy sessions so that they return to as new condition, prolonging the life of the shoes. Content: Satin Features: Wide Square Box Long Vamp Low Profile Flexible Shank Pliable Shank Strength Molding Box Strength Square Foot Shape


"We are trying to create a method for a robot to seek help from the whole world when it is puzzled by something," said Rajesh Rao, associate professor of computer science and engineering at the University of Washington in US.

"This is a way to go beyond just one-on-one interaction between a human and a robot by also learning from other humans around the world," Rao added. The common practice of asking a robot to learn by imitating a human to perform tasks can take a lot of time but if a robot can learn the basic steps of a task, then it may ask the online community for additional input or data on how to complete this task efficiently and correctly.

"Because our robots use machine-learning techniques, they require a lot of data to build accurate models of the task. The more data they have, the better model they can build. Our solution is to get that data from crowd sourcing," said Maya Cakmak, assistant professor of computer science and engineering, at the varsity.

For the study, researchers asked the online crowd sourcing community to teach a robot a model-building task.

To begin with, the online participants built a simple model out of coloured Lego blocks and then they asked the robot to build a similar object.They hired people on Amazon Mechanical Turk, a crowd sourcing site, to build similar models of a car, tree, turtle, snake and others. From more than 100 crowd-generated models, the robot searched for the best models to build a new and simple model. This type of learning is called ‘goal-based imitation’.

The results were presented at the Institute of Electrical and Electronics Engineers International Conference on Robotics and Automation in Hong Kong earlier this month.

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