Fax is an efficiency killer but survives. Why?
3 previous posts show the Question posed has a premise. Fax is not an efficiency killer - but can be far more efficient than Email FTP Messaging etc. via internet ... when the problem being posed is considered accurately. Single sender to single recipient spur of the moment need to transmit a document FAX is FAR MORE EFFICIENT. COST EFFICIENCY? Monetary Years ago - $.5-$.15 for phone line use was not unusual for domestic long distances. Today unlimited calling nationwide makes monetary costs difference zero. We have band to burn. Moderate latency costs Fax scanning and printing out same latency issues - break even with scanning and printing. Receiver can collect and store electronically if they do not wish hard copy same as scan. (stock trading - dead disadvantage notwithstanding) REALITY many businesses still have manual workflow. Hardcopy is integral to their model of business. FAX is far more efficient for moderate sized docs sent infrequently between a small business and large number of customers from a large population of infrequent customers. A physical Faxed Doc sitting in a output bin is also a visual cue spurring action for office workers away from their desks. People and processes still exist in the physical domain from time to time during a well designed work day! Privacy Doctor's office insisted on fax transmission and they could not (would not) receive a scanned doc via email. HIPPA rules on privacypliance concerns? HIPAA Compliance Tips for Medical Practices Or is it just arcane reality of a dying breed? As long as workflow remains in the physical domain FAX can be safer and sometimes far more efficient. For the time being FAX machines will persist. nThere is an entire market segment set up around fax services through email. These will curiously persist well beyond physical fax machines disappearing ... Then the QUORA posted question will ask Why do inefficient FAX protocols persist after the last fax machine went out ofmission? Inertia - what a concept.
What are the main tools that I should use during a CTF contest in all categories?
What are some interesting summer programming projects that one can do?
If you are interested in applied math a little and where it relates to CS (e.g.puter graphicsputational science optimization AI) you could try implementing some canonical problems in these areas. I think it's a good idea for people to learn math. What's better is that you have a way of learning it that others may not--through programming a solution. Even better you get to see how other people did it too. More importantly mathematical literacy shows a great skill that people who know anything about anything will see as a plus so put it on GitHub. Here are some things Code up a Newton-Raphson gradient descent simulated annealing or genetic algorithm methods for optimization Write a simple diffusion equation solver on a flat 2D (or 1D) grid Implement k-means clustering some simple Markov model or linear SVM (you can cheat here a little and just use some optimization libraries) Write up a Fourier transform routine then do the fast Fourier transform. Or Walsch-Hadamard whatever. Write a simple linear algebra library (i.e. addition matrix-matrix matrix-vector multiplication inner and outer products tensor products matrix inversion solving linear systems and really however far you want to go with it. if you're really into it try eigendposition or variations on these other methods for particular matrices.. if you've gone this far would be advisable to pick up a numerical linear algebra book) Write a simple quantum circuit simulator (initial state apply gates (read operators) to it check results) and implement some basic cool algorithms. Might want to pick up a book on this but not necessary. n ** Bonus points if you do a multithreaded distributed memory GPU or low-level optimized (e.g. SSE) implementation. I think that would be a good thing for anyone to see. And don't forget to put it on your GitHub dammit! I think this is a great chance to get acclimated with mathematics. Lots of people use the above things or moreplicated versions of them. Math is all the same in your head you just need to start somewhere. Once you know how to reason about a Fourier transform you might further understand how to solve certain ODEs or if you understand eigendposition you might understand dimensionality reduction in ML. From there you can build towards a good understanding of mathematics and how you can exploit it in your everyday problems. Good luck! If you decide to tackle any of these problems feel free toment for books or further resources.