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Neon Bulb Oscillators

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Neon Tube Oscillators  Fig. 1 High voltage to headphones, don't do this in modern times. Very high impedance headphones are implied. A neon bulb is useful in many ways in an amateur station, but there are undoubtedly many amateurs who are not aware of the fact that the device can be made to oscillate at either audio or radio frequencies. Fig. 1 shows how a single bulb may be hooked up as an audio frequency oscillator. The L-C circuit is formed by the headphones and a condenser, and by varying the capacity the circuit can be made to oscillate at frequencies from a few cycles per second up to the highest audio frequencies.  The variable resistor in series with the bulb and battery controls the strength of oscillation, and also controls the frequency to some extent. A 1 nf variable condenser will be sufficient to cover most of the audio range.  Fig. 2 The circuit in Fig. 2 may be used when an RF oscillator is to be modulated at audio frequencies. The transformer may be an or...
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A practical way to view neural networks (whiteboard view)

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 A whiteboard view: You can take a decision matrix view of ReLU neural networks. Instead of ReLU(Wx) you create a diagonal matrix with binary 1 or 0 entries according to the ReLU decisions (x>=0?) Then conceptually a layer is DWx. Where D is doing row selection on W. And in fact D does column selection on the weight matrix in the next layer. That is very coarse parameter selection which is linear mapping selection.
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CCSLM via the ReLU entry point - Flyer

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  CCSLM via the ReLU entry point Break a ReLU neural network into a main data channel and a context channel . ReLU(Wx) = DWx , where D is a diagonal decision matrix containing 0/1 entries produced by a context function applied to the layer input—in this case, the ReLU decisions. The main data channel therefore only ever sees linear mappings : the W matrices and the D selection matrices. This is a remarkable restriction on apparent expressiveness, but it has potentially very positive consequences. The context channel is doing something quite different. It does not transform the data; it selects which linear mapping acts on the data . A sequence of binary decisions across layers can represent an exponentially large number of distinct symbolic contexts or computational paths, even though each individual decision is simple. The selected mapping can be made explicit by fusing D with an adjacent weight matrix. L = WD is the more useful representation for human comprehension: the co...
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