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In problem (P-T), the model variable C K × T could be very large, making the direct solution of (P-T) computationally expensive. Unlike problem (P-S), the problem (P-T) cannot be readily parallelized because the constraints G ( i ) 𝒄 i 0 couple the entries within each row of C, and the residual term couples entries across columns. Here, we follow the block coordinate-descent approach used in ( Pnevmatikakis et al., 2016 ) and propose an algorithm that sequentially updates each 𝒄 i and 𝒃 0 . For each neuron, we start with a simple unconstrained estimate of 𝒄 i , denoted as 𝒚 i ^ , that minimizes the residual of the spatiotemporal data matrix while fixing other neurons’ spatiotemporal activity and the baseline term 𝒃 0 ,

(9)

where Y res = Y A ^ C ^ b ^ 0 1 T B f represents the residual given the current estimate of the model variables. Due to its unconstrained nature, y ^ i is a noisy estimate of 𝒄 i , plus a constant baseline resulting from inaccurate estimation of 𝒃 0 . Given 𝒚 ^ i , various deconvolution algorithms can be applied to obtain the denoised trace 𝒄 ^ i and deconvolved signal 𝒔 ^ i ( Vogelstein et al., 2009 ; Pnevmatikakis et al., 2013 ; 2016 New Style high quality comfortable wholesale running sport shoes Cheap Sale Fashionable Buy Cheap Really CTQwSsDIzI
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; Jewell and Witten, 2017 ); in CNMF-E, we use the OASIS algorithm from ( Friedrich et al., 2017b ). (Note that the estimation of 𝒄 i is not dependent on accurate estimation of 𝒃 0 , because the algorithm for estimating 𝒄 i will also automatically estimate the baseline term in 𝒚 ^ i .) After the 𝒄 i ’s are updated, we update 𝒃 0 using the closed-form expression 𝒃 ^ 0 = 1 T ( Y ~ - A ^ C ^ - B ^ f ) 𝟏 .

Whenever we deal with other developers, but only if there’s lack of a wider standard, we should at least aim for level 2 consistency, that is, respect their code. We touch something in their domain, we write it like they would do.

When we are in a bigger organization — though "bigger" can truly start at two people — this same idea of level 2 consistency prevails, but we can now think of setting up standards to operate at level 3. There, we can even marry the two levels: Follow the coding guidelines, but when we touch something that violates the guidelines and we don’t have the time to reformat it, we follow the style prevalent in that code.

From my experience, being aware of these levels alone helps a great deal writing more consistent, and with that quite better code.

If you like to learn more about coding standards, check out other CSS-Tricks posts about the topic , and if you like a short, very short read about them, perhaps also The Little Book of HTML/CSS Coding Guidelines .

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Do you design digital products (or websites) and hand design files off to developers for implementation? If you answered yes, settle in! While the should-designers-code debate rages on, we're going to look at how adding a methodology to your design workflow can make you faster, more consistent, and loved by…

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Setting CSS variables to theme a design system can be tricky: if they are too scoped, the system will lose consistency. If they are too global, you lose granularity. Maybe we can fix both issues. I’d like to try to boil design system variables down to two types: Global and…

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The following is a guest post by Tom Genoni. Tom is going to introduce us to the thinking and process behind Optimizely's new UI library / Sass framework. Part 2, by Daniel O'Connor, looks at some of the technical and integration bits. When I first started working on web projects,…

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I’d like to disagree with this if I can. I guess it’s debatable, but it seems like the first step towards forgetting/ignoring the standards completely.

but when we touch something that violates the guidelines and we don’t have the time to reformat it, we follow the style prevalent in that code.

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A string conversion is an expression list enclosed in reverse (a.k.a. backward) quotes:

A string conversion evaluates the contained expression list and converts the resulting object into a string according to rules specific to its type.

If the object is a string, a number, , or a tuple, list or dictionary containing only objects whose type is one of these, the resulting string is a valid Python expression which can be passed to the built-in function to yield an expression with the same value (or an approximation, if floating point numbers are involved).

(In particular, converting a string adds quotes around it and converts “funny” characters to escape sequences that are safe to print.)

Recursive objects (for example, lists or dictionaries that contain a reference to themselves, directly or indirectly) use to indicate a recursive reference, and the result cannot be passed to to get an equal value ( will be raised instead).

The built-in function performs exactly the same conversion in its argument as enclosing it in parentheses and reverse quotes does. The built-in function performs a similar but more user-friendly conversion.

string_conversion yield_atom yield_expression

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expression in a function definition is sufficient to cause that definition to create a generator function instead of a normal function.

When a generator function is called, it returns an iterator known as a generator. That generator then controls the execution of a generator function. The execution starts when one of the generator’s methods is called. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to generator’s caller. By suspended we mean that all local state is retained, including the current bindings of local variables, the instruction pointer, and the internal evaluation stack. When the execution is resumed by calling one of the generator’s methods, the function can proceed exactly as if the yield expression was just another external call. The value of the yield expression after resuming depends on the method which resumed the execution.

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