Yes or no
Answers, anytime
Now that you have your answer, look it up in the second column of the table. The text in that row corresponds to different outcomes, depending on the true answer. Of course, you do not know the true answer (if you did, you wouldn't be using our service), you are well on your way to solving your problem with our answer.
| universal best course of action | ||||
|---|---|---|---|---|
| yes | no | maybe | ||
| our answer | yes | Our methods have correctly determined that affirmative action is the correct route. Hold your course and reap the rewards. | This is a rare opportunity to experience what most do not attempt. We encourage you to be adventurous, daring, and cavalier. | The universe does not currently support experimental verification of this course of action. In its paradigm, quantum mechanics uses superposition of states, interpreted as both yes and no states simultaneously, but such states are not observables. Although we provide the answer for you, you must wait until the universe becomes compatible with these kinds of predictions. |
| no | While others may be tempted to act, we urge caution and healthy skepticism. This is a good time to stand back and reconsider or take up a new hobby. | General truths in this case are negative and you are certain to avoid them. Others will fail where you will persevere. Not to act is an action. | ||
| maybe | Although to us the answer is clear, the time is not right to reveal it. Consider your current situation and act accordingly. See this as a chance to gain perspective on an old problem. | You naturally wish to act, motivated by the momentum of past successes. Take caution. Looking in the rear view mirror can often tell you where you are going (not only when going backwards). | We cannot fit the explanation of this combination here. For the time being, you are to consider the probability of this combination infinitely small | |
For more details, see the about section.
Convolutional neural networks
Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry. – Richard Feynman
Following up on our Neural network primer column, this month we explore a different kind of network architecture: a convolutional network.
The convolutional network replaces the hidden layer of a fully connected network (FCN) with one or more filters (a kind of neuron that looks at the input within a narrow window).
Even through convolutional networks have far fewer neurons that an FCN, they can perform substantially better for certain kinds of problems, such as sequence motif detection.
Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Convolutional neural networks. Nature Methods 20:.
Background reading
Derry, A., Krzywinski, M. & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.
Neural network primer
Nature is often hidden, sometimes overcome, seldom extinguished. —Francis Bacon
In the first of a series of columns about neural networks, we introduce them with an intuitive approach that draws from our discussion about logistic regression.
Simple neural networks are just a chain of linear regressions. And, although neural network models can get very complicated, their essence can be understood in terms of relatively basic principles.
We show how neural network components (neurons) can be arranged in the network and discuss the ideas of hidden layers. Using a simple data set we show how even a 3-neuron neural network can already model relatively complicated data patterns.
Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.
Background reading
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.
Cell Genomics cover
Our cover on the 11 January 2023 Cell Genomics issue depicts the process of determining the parent-of-origin using differential methylation of alleles at imprinted regions (iDMRs) is imagined as a circuit.
Designed in collaboration with with Carlos Urzua.
Akbari, V. et al. Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq (2023) Cell Genomics 3(1).
Browse my gallery of cover designs.
