Us: using sexy pickup lines for inducing a stranger to sleep with us.

Ifeoluwanimi Adebara, an intellectual:

at my parents' eating cheese, ready to go on a long stay somewhere cold.

this is my therapy

the actor who plays homelander really manages to make every act of drinking milk uniquely grotesque and menacing

implementing some of the concepts from in WebGL and js. Here's a rendered approximation of CIE XYZ colour. It's a little off right now, and the effect is too much smearing of the center white to the edges.

I'll try to write up more in a blog post tomorrow, and hopefully publish the code.

Neat talk by on changes in design thinking. Check out the book "how to speak machine"

with at giving a talk on taking on new tech

- react, elm, Haskell: new, nonstandard tech
- all risks: no ecosystem, learning curve, hiring
- controlled experiment: low-risk project, get it into prod, expand or back out
- react rewards: less bugs, maintainable code, grow as devs
- elm rewards: move quickly w/o breaking, reliable front-end, grow as dev
- Haskell rewards: easier to maintain complex biz logic, less runtime errs

keynote by Jessica Kerr. I learned a new word: symmathesize.

Noise-adaptive compilation
Using reported gate/qubit error rates to improve runtime accuracy

- not just limiting circuit depth, but prioritizing qubits and connections based on measured error
- scaffold to IR, then uses smt and error data to optimize final openQasm output for given day
- smt scales to 72qbits, hoping heuristics based will scales to 1000s
- improvement over qiskit

- T1 is modelled as hard cutoff, not exp decay

Oh crap he's back.
That duck must have been underwater for *minutes*.

Zeige Konversation

Splitting CNN for better efficiency

- split CNNs into tiles across GPUs, less memory on/offloading
- costs in quality of model, changes semantics
- mitigate with stochastic splitting, regularizes data
- some scheduling to see where cost of splitting vs time of offloading

"Split-CNN: Splitting Window-based Operations in Convolutional Neural Networks for Memory System Optimization"

BitTactical : combined approaches to improve NN computation by skipping zeros

Frontend, scheduling
- "fill in" weights of zero with non-zero weight/activation muls
- previous efforts searched anywhere in pending muls, restricting depth/breadth of search improves perf

Backend, not as clear on this:
- bit serial comp allows to focus on specific bits, precise mul
- smaller granularity 8 vs 16 bit results in less mul due to sparse bits

Older link

Mega-microfluidics: speculative WACI talk about scaling up micro-fluidics

- parallelizing boards
- routing and data-movement is extremely expensive in terms of time, have to pipe from board to board
- cross-contamination and isolation of fluidic experiments

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