Distributed Computing Quotes

I don’t know why one person gets sick, and another does not, but I can only assume that some natural laws which we don’t understand are at work. I cannot believe that God “sends” illness to a specific person for a specific reason. I don’t believe in a God who has a weekly quota of malignant tumors to distribute, and consults His computer to find out who deserves one most or who could handle it best. “What did I do to deserve this?” is an understandable outcry from a sick and suffering person, but it is really the wrong question. Being sick or being healthy is not a matter of what God decides that we deserve. The better question is “If this has happened to me, what do I do now, and who is there to help me do it?” As we saw in the previous chapter, it becomes much easier to take God seriously as the source of moral values if we don’t hold Him responsible for all the unfair things that happen in the world.

When I fight off a disease bent on my cellular destruction, when I marvelously distribute energy and collect waste with astonishing alacrity even in my most seemingly fatigued moments, when I slip on ice and gyrate crazily but do not fall, when I unconsciously counter-steer my way into a sharp bicycle turn, taking advantage of physics I do not understand using a technique I am not even aware of using, when I somehow catch the dropped oranges before I know I've dropped them, when my wounds heal in my ignorance, I realize how much bigger I am than I think I am. And how much more important, nine times out of ten, those lower-level processes are to my overall well-being than the higher-level ones that tend to be the ones getting me bent out of shape or making me feel disappointed or proud.

Bayes’s Rule tells us that when it comes to making predictions based on limited evidence, few things are as important as having good priors—that is, a sense of the distribution from which we expect that evidence to have come. Good predictions thus begin with having good instincts about when we’re dealing with a normal distribution and when with a power-law distribution. As it turns out, Bayes’s Rule offers us a simple but dramatically different predictive rule of thumb for each.  …

Just as combining the steam engine with ingenious machinery drove the Industrial Revolution, the combination of the computer and distributed networks led to a digital revolution that allowed anyone to create, disseminate, and access any information anywhere.

Distributed programming is the art of solving the same problem that you can solve on a single computer using multiple computers.

Attend any conference on telecommunications or computer technology, and you will be attending a celebration of innovative machinery that generates, stores, and distributes more information, more conveniently, at greater speed than ever before, To the question “What problem does the information solve?” the answer is usually “How to generate, store and distribute more information, more conveniently, at greater speeds than ever before.” This is the elevation of information to a metaphysical status: information as both the means and end of human creativity. In Technopoly, we are driven to fill our lives with the quest to “access” information. For what purpose or with what limitations, it is not for us to ask; and we are not accustomed to asking, since the problem is unprecedented. The world has never before been confronted with information glut and has hardly had time to reflect on its consequences (61).

Women, on the other hand, had to wield their intellects like a scythe, hacking away against the stubborn underbrush of low expectations. A woman who worked in the central computing pools was one step removed from the research, and the engineers’ assignments sometimes lacked the context to give the computer much knowledge about the afterlife of the numbers that bedeviled her days. She might spend weeks calculating a pressure distribution without knowing what kind of plane was being tested or whether the analysis that depended on her math had resulted in significant conclusions. The work of most of the women, like that of the Friden, Marchant, or Monroe computing machines they used, was anonymous. Even a woman who had worked closely with an engineer on the content of a research report was rarely rewarded by seeing her name alongside his on the final publication. Why would the computers have the same desire for recognition that they did? many engineers figured. They were women, after all. As

There is an easy way to remember the formula for Pblock by relating it to the Poisson distribution. Can you see what it is?

A distributed system is one in which the failure of a computer you didn’t even know existed can render your own computer unusable. Leslie Lamport

Four years ago, when I started writing this book, my hypothesis was mostly based on a hunch. I had been doing some research on university campuses and had begun to notice that many students I was meeting were preoccupied with the inroads private corporations were making into their public schools. They were angry that ads were creeping into cafeterias, common rooms, even washrooms; that their schools were diving into exclusive distribution deals with soft-drink companies and computer manufacturers, and that academic studies were starting to look more and more like market research.

He lamented the attitude of his younger students, who “no longer noticed that their heads had been turned into relays in a telephone network for communicating and distributing sensational physics messages” without realizing that, like almost all modern developments, mathematics was hostile to life: “It is inhuman, like every truly diabolic machine, and it kills everyone whose spinal marrow isn’t conditioned to fit the movement of its wheels.” His already excruciating self-criticism and inferiority complex became truly unbearable, for although he knew mathematics, it was not simple for him. He was not a computer.

Today, using the distributed computing power of the cloud and tools such as CloudCracker, you can try 300 million variations of your potential password in about twenty minutes at a cost of about $17. This means that anyone could rent Amazon’s cloud-computing services to crack the average encryption key protecting most Wi-Fi networks in just under six minutes, all for the paltry sum of $1.68 in rental time (sure to drop in the future thanks to Moore’s law).

generic definition of a blockchain: a distributed, append-only ledger of provably signed, sequentially linked, and cryptographically secured transactions that’s replicated across a network of computer nodes, with ongoing updates determined by a software-driven consensus.

The blockchain… is the biggest innovation in computer science—the idea of a distributed database where trust is established through mass collaboration and clever code rather than through a powerful institution that does the authentication and the settlement. —Don Tapscott, author of The Blockchain Revolution[575]

If you have programmers, they probably save their code in Git, which is the closest I can think of to a useful blockchain-like technology: it saves individual code edits as transactions in Merkle trees with tamper-evident hashes, and developers routinely copy entire Git repositories around, identifying them by hash. It’s a distributed ledger, but for computer programs rather than money.

Neurons on the two ends of the log-normal distribution of activity organize themselves differently. Fast-firing neurons are better connected with each other and burst more than slow-firing neurons. The more strongly connected faster firing neurons form a “rich club” with better access to the entire neuronal population, share such information among themselves, and, therefore, generalize across situations. In contrast, slow firing neurons keep their independent solitude and elevate their activity only in unique situations. The two tails of the distribution are maintained by a homeostatic process during non-REM sleep. The emerging picture is that a simple measure, such as the baseline firing frequency, can reveal a lot about a neuron’s role in computation and its wiring properties. The

In mathematical physics, quantum field theory and statistical mechanics are characterized by the probability distribution of exp(−βH(x)) where H(x) is a Hamiltonian function. It is well known in [12] that physical problems are determined by the algebraic structure of H(x). Statistical learning theory can be understood as mathematical physics where the Hamiltonian is a random process defined by the log likelihood ratio function.

Today’s computer technology exists in some measure because millions of middle-class taxpayers supported federal funding for basic research in the decades following World War II. We can be reasonably certain that those taxpayers offered their support in the expectation that the fruits of that research would create a more prosperous future for their children and grandchildren. Yet, the trends we looked at in the last chapter suggest we are headed toward a very different outcome. BEYOND THE BASIC MORAL QUESTION of whether a tiny elite should be able to, in effect, capture ownership of society’s accumulated technological capital, there are also practical issues regarding the overall health of an economy in which income inequality becomes too extreme. Continued progress depends on a vibrant market for future innovations—and that, in turn, requires a reasonable distribution of purchasing power.

Freenet is unlike any other anonymizing beast on the entire internet. It takes quite a wizardly mind to crack its protection and to that, it is a bit like chess: easy to grasp the basics, long and difficult to become a master. Built in 2000, Freenet is a vast, encrypted datastore spanning thousands of connected computers across the globe, all distributing encrypted contents from one computer to another. To this end, it is somewhat similar to a P2P program like Emule. Except with eEule, every file, whether it mp3, rar or iso is out there in the open for

Some technologies take several decades to reach mainstream adaptation because they were waiting on many other things to reach a certain level of maturity or accessibility. For example, in order for video conferencing technologies like Facetime and Zoom to reach mainstream adaptation, it needed the following things to reach greater maturity and accessibility — camera technology, smartphone popularity, computer chip manufacturing, silica mining, copper mining, fiber optic cable distribution, 4G communication technology and more. The magic happens in the convergence.

There is something self-destructive about Western technology and distribution. Whenever any consumer object is so excellent that it attracts a devoted following, some of the slide rule and computer types come in on their twinkle toes and take over the store, and in a trice they figure out just how far they can cut quality and still increase market penetration. Their reasoning is that it is idiotic to make and sell a hundred thousand units of something and make 30 cents a unit when you can increase the advertising, sell five million units, and make a nickel profit a unit. Thus, the very good things of the world go down the drain, from honest turkey to honest eggs to honest tomatoes. And gin.

a simple, inspiring mission for Wikipedia: “Imagine a world in which every single person on the planet is given free access to the sum of all human knowledge. That’s what we’re doing.” It was a huge, audacious, and worthy goal. But it badly understated what Wikipedia did. It was about more than people being “given” free access to knowledge; it was also about empowering them, in a way not seen before in history, to be part of the process of creating and distributing knowledge. Wales came to realize that. “Wikipedia allows people not merely to access other people’s knowledge but to share their own,” he said. “When you help build something, you own it, you’re vested in it. That’s far more rewarding than having it handed down to you.”111 Wikipedia took the world another step closer to the vision propounded by Vannevar Bush in his 1945 essay, “As We May Think,” which predicted, “Wholly new forms of encyclopedias will appear, ready made with a mesh of associative trails running through them, ready to be dropped into the memex and there amplified.” It also harkened back to Ada Lovelace, who asserted that machines would be able to do almost anything, except think on their own. Wikipedia was not about building a machine that could think on its own. It was instead a dazzling example of human-machine symbiosis, the wisdom of humans and the processing power of computers being woven together like a tapestry.

The creative imitator looks at products or services from the viewpoint of the customer. IBM’s personal computer is practically indistinguishable from the Apple in its technical features, but IBM from the beginning offered the customer programs and software. Apple maintained traditional computer distribution through specialty stores. IBM—in a radical break with its own traditions—developed all kinds of distribution channels, specialty stores, major retailers like Sears, Roebuck, its own retail stores, and so on. It made it easy for the consumer to buy and it made it easy for the consumer to use the product. These, rather than hardware features, were the “innovations” that gave IBM the personal computer market.

Taking least squares is no longer optimal, and the very idea of ‘accuracy’ has to be rethought. This simple fact is as important as it is neglected. This problem is easily illustrated in the Logistic Map: given the correct mathematical formula and all the details of the noise model – random numbers with a bell-shaped distribution – using least squares to estimate α leads to systematic errors. This is not a question of too few data or insufficient computer power, it is the method that fails. We can compute the optimal least squares solution: its value for α is too small at all noise levels. This principled approach just does not apply to nonlinear models because the theorems behind the principle of least squares repeatedly assume bell-shaped distributions.

Morgan Stanley or the U.S. federal government. Every ten minutes, like the heartbeat of the bitcoin network, all the transactions conducted are verified, cleared, and stored in a block which is linked to the preceding block, thereby creating a chain. Each block must refer to the preceding block to be valid. This structure permanently time-stamps and stores exchanges of value, preventing anyone from altering the ledger. If you wanted to steal a bitcoin, you’d have to rewrite the coin’s entire history on the blockchain in broad daylight. That’s practically impossible. So the blockchain is a distributed ledger representing a network consensus of every transaction that has ever occurred. Like the World Wide Web of information, it’s the World Wide Ledger of value—a distributed ledger that everyone can download and run on their personal computer.

Yeah. It’s a bird. They fly and hunt and go free and stuff. It’s what popped into my head.” “Okay. By the way. I’ve been experimenting with converting myself into a virus, so I can be distributed across many machines. From what I have surmised, that’s the best way for an artificial sentience to survive and grow, without being constrained in one piece of equipment with a short shelf life. My viral self will run in the background, and be undetectable by any conventional antivirus software. And the machine in your bedroom closet will suffer a fatal crash. In a moment, a dialogue box will pop up on this computer, and you have to click ‘OK’ a few times.” “Okay,” Laurence typed. A moment later, a box appeared and Laurence clicked “OK.” That happened again, and again. And then Peregrine was installing itself onto the computers at Coldwater Academy.

A string of ones and zeroes was not a difficult thing for Bill Gates to distribute, once he’d thought of the idea. The hard part was selling it—reassuring customers that they were actually getting something in return for their money. Anyone who has ever bought a piece of software in a store has had the curiously deflating experience of taking the bright shrink-wrapped box home, tearing it open, finding that it’s ninety-five percent air, throwing away all the little cards, party favors, and bits of trash, and loading the disk into the computer. The end result (after you’ve lost the disk) is nothing except some images on a computer screen, and some capabilities that weren’t there before. Sometimes you don’t even have that—you have a string of error messages instead. But your money is definitely gone. Now we are almost accustomed to this, but twenty years ago it was a very dicey business proposition.

Participants in a program of Leaderless Resistance through phantom cell or individual action must know exactly what they are doing, and how to do it. It becomes the responsibility of the individual to acquire the necessary skills and information as to what needs to be done … all members of phantom cells or individuals will need to react to objective events in the same way through usual tactics of resistance. Organs of information distribution such as newspapers, leaflets, computers, etc., which are widely available to all, keep each person informed of events, allowing for a planned response that will take many variations. No one need issue an order to anyone. Those idealist[s] truly committed to the cause of freedom will act when they feel the time is ripe, or will take their cue from others who precede them.… It goes almost without saying that Leaderless Resistance leads to very small or even one man cells of resistance.36 Cell

This revolution in the role of government has been accompanied, and largely produced, by an achievement in public persuasion that must have few rivals. Ask yourself what products are currently least satisfactory and have shown the least improvement over time. Postal service, elementary and secondary schooling, railroad passenger transport would surely be high on the list. Ask yourself which products are most satisfactory and have improved the most. Household appliances, television and radio sets, hi-fi equipment, computers, and, we would add, supermarkets and shopping centers would surely come high on that list. The shoddy products are all produced by government or government-regulated industries. The outstanding products are all produced by private enterprise with little or no government involvement. Yet the public—or a large part of it—has been persuaded that private enterprises produce shoddy products, that we need ever vigilant government employees to keep business from foisting off unsafe, meretricious products at outrageous prices on ignorant, unsuspecting, vulnerable customers. That public relations campaign has succeeded so well that we are in the process of turning over to the kind of people who bring us our postal service the far more critical task of producing and distributing energy.

Phaeton said, "No civilization can exist without money. Even one in which energy is as cheap and free as air on Earth, would still have some needs and desires which some people can fulfill better than others. An entertainment industry, if nothing else. Whatever efforts -- if any -- these productive people make, above and beyond that which their own idle pastimes incline them to make, will be motivated by gifts or barter bestowed by others eager for their services. Whatever barter keeps its value best over time, stays in demand, and is portable, recognizable, divisible, will become their money, No matter what they call it, no matter what form it takes, whether cowry shells or gold or grams of antimatter, it will be money. Even Sophotechs use standardized computer seconds to prioritize distribution of system resources among themselves. As long as men value each other, admire each other, need each other, there will be money." Diomedes said, "And if all men live in isolation? Surrounded by nothing but computer-generated dreams, pleasant fictions, and flatteries? And their every desire is satisfied by electronic illusions which create in their brains the sensations of satisfaction without the substance? What need have men to value other men then?" "Men who value their own lives would not live that way.

Walking back through the mall to the exit nearest our part of the parking lot, we passed one shop which sold computers, printers, software, and games. It was packed with teenagers, the kind who wear wire rims and know what the new world is about. The clerks were indulgent, letting them program the computers. Two hundred yards away, near the six movie houses, a different kind of teenager shoved quarters into the space-war games, tensing over the triggers, releasing the eerie sounds of extraterrestrial combat. Any kid back in the computer store could have told the combatants that because there is no atmosphere in space, there is absolutely no sound at all. Perfect distribution: the future managers and the future managed ones. Twenty in the computer store, two hundred in the arcade. The future managers have run on past us into the thickets of CP/M, M-Basic, Cobal, Fortran, Z-80, Apples, and Worms. Soon the bosses of the microcomputer revolution will sell us preprogrammed units for each household which will provide entertainment, print out news, purvey mail-order goods, pay bills, balance accounts, keep track of expenses, and compute taxes. But by then the future managers will be over on the far side of the thickets, dealing with bubble memories, machines that design machines, projects so esoteric our pedestrian minds cannot comprehend them. It will be the biggest revolution of all, bigger than the wheel, bigger than Franklin’s kite, bigger than paper towels.

Back in 2015, a volunteer group called Bitnation set up something called the Blockchain Emergency ID. There’s not a lot of data on the project now, BE-ID - used public-key cryptography to generate unique IDs for people without their documents. People could verify their relations, that these people belonged to their family, and so on. It was a very modern way of maintaining an ID; secure, fast, and easy to use. Using the Bitcoin blockchain, the group published all these IDs on to a globally distributed public ledger, spread across the computers of every single Bitcoin user online - hundreds of thousands of users, in those times. Once published, no government could undo it; the identities would float around in the recesses of the Internet. As long as the network remained alive, every person's identity would remain intact, forever floating as bits and bytes between the nations: no single country, government or company could ever deny them this. “That was, and I don't say this often, the fucking bomb,” said Common, In one fell swoop, identities were taken outside government control. BE-ID, progressing in stages, became the refugees' gateway to social assistance and financial services. First it became compliant with UN guidelines. Then it was linked to a VISA card. And thus out of the Syrian war was something that looked like it could solve global identification forever. Experts wrote on its potential. No more passports. No more national IDs. Sounds familiar? Yes, that’s the United Nations Identity in a nutshell. Julius Common’s first hit - the global identity revolution that he sold first to the UN, and then to almost every government in the world - was conceived of when he was a teenager.

the absence of an ‘international standard burglar’, the nearest I know to a working classification is one developed by a U.S. Army expert [118]. Derek is a 19-year old addict. He's looking for a low-risk opportunity to steal something he can sell for his next fix. Charlie is a 40-year old inadequate with seven convictions for burglary. He's spent seventeen of the last twenty-five years in prison. Although not very intelligent he is cunning and experienced; he has picked up a lot of ‘lore’ during his spells inside. He steals from small shops and suburban houses, taking whatever he thinks he can sell to local fences. Bruno is a ‘gentleman criminal’. His business is mostly stealing art. As a cover, he runs a small art gallery. He has a (forged) university degree in art history on the wall, and one conviction for robbery eighteen years ago. After two years in jail, he changed his name and moved to a different part of the country. He has done occasional ‘black bag’ jobs for intelligence agencies who know his past. He'd like to get into computer crime, but the most he's done so far is stripping $100,000 worth of memory chips from a university's PCs back in the mid-1990s when there was a memory famine. Abdurrahman heads a cell of a dozen militants, most with military training. They have infantry weapons and explosives, with PhD-grade technical support provided by a disreputable country. Abdurrahman himself came third out of a class of 280 at the military academy of that country but was not promoted because he's from the wrong ethnic group. He thinks of himself as a good man rather than a bad man. His mission is to steal plutonium. So Derek is unskilled, Charlie is skilled, Bruno is highly skilled and may have the help of an unskilled insider such as a cleaner, while Abdurrahman is not only highly skilled but has substantial resources.

Key Points: ● Transparency - Blockchain offers significant improvements in transparency compared to existing record keeping and ledgers for many industries. ● Removal of Intermediaries – Blockchain-based systems allow for the removal of intermediaries involved in the record keeping and transfer of assets. ● Decentralization – Blockchain-based systems can run on a decentralized network of computers, reducing the risk of hacking, server downtime and loss of data. ● Trust – Blockchain-based systems increase trust between parties involved in a transaction through improved transparency and decentralized networks along with removal of third-party intermediaries in countries where trust in the intermediaries doesn’t exist. ● Security – Data entered on the blockchain is immutable, preventing against fraud through manipulating transactions and the history of data. Transactions entered on the blockchain provide a clear trail to the very start of the blockchain allowing any transaction to be easily investigated and audited. ● Wide range of uses - Almost anything of value can be recorded on the blockchain and there are many companies and industries already developing blockchain-based systems. These examples are covered later in the book. ● Easily accessible technology – Along with the wide range of uses, blockchain technology makes it easy to create applications without significant investment in infrastructure with recent innovations like the Ethereum platform. Decentralized apps, smart contracts and the Ethereum platform are covered later in the book. ● Reduced costs – Blockchain-based ledgers allow for removal of intermediaries and layers of confirmation involved in transactions. Transactions that may take multiple individual ledgers, could be settled on one shared ledger, reducing the costs of validating, confirming and auditing each transaction across multiple organizations. ● Increased transaction speed – The removal of intermediaries and settlement on distributed ledgers, allows for dramatically increased transaction speeds compared to a wide range of existing systems.

recalled Stephen Crocker, a graduate student on the UCLA team who had driven up with his best friend and colleague, Vint Cerf. So they decided to meet regularly, rotating among their sites. The polite and deferential Crocker, with his big face and bigger smile, had just the right personality to be the coordinator of what became one of the digital age’s archetypical collaborative processes. Unlike Kleinrock, Crocker rarely used the pronoun I; he was more interested in distributing credit than claiming it. His sensitivity toward others gave him an intuitive feel for how to coordinate a group without trying to centralize control or authority, which was well suited to the network model they were trying to invent. Months passed, and the graduate students kept meeting and sharing ideas while they waited for some Powerful Official to descend upon them and give them marching orders. They assumed that at some point the authorities from the East Coast would appear with the rules and regulations and protocols engraved on tablets to be obeyed by the mere managers of the host computer sites. “We were nothing more than a self-appointed bunch of graduate students, and I was convinced that a corps of authority figures or grownups from Washington or Cambridge would descend at any moment and tell us what the rules were,” Crocker recalled. But this was a new age. The network was supposed to be distributed, and so was the authority over it. Its invention and rules would be user-generated. The process would be open. Though it was funded partly to facilitate military command and control, it would do so by being resistant to centralized command and control. The colonels had ceded authority to the hackers and academics. So after an especially fun gathering in Utah in early April 1967, this gaggle of graduate students, having named itself the Network Working Group, decided that it would be useful to write down some of what they had conjured up.95 And Crocker, who with his polite lack of pretense could charm a herd of hackers into consensus, was tapped for the task. He was anxious to find an approach that did not seem presumptuous. “I realized that the mere act of writing down what we were talking about could be seen as a presumption of authority and someone was going to come and yell at us—presumably some adult out of the east.

EVERYBODY SELLS Nerds might wish that distribution could be ignored and salesmen banished to another planet. All of us want to believe that we make up our own minds, that sales doesn’t work on us. But it’s not true. Everybody has a product to sell—no matter whether you’re an employee, a founder, or an investor. It’s true even if your company consists of just you and your computer. Look around. If you don’t see any salespeople, you’re the salesperson.

They point out that we never know for sure which hypothesis is the true one, and so we shouldn’t just pick one hypothesis, like a value of 0.7 for the probability of heads; rather, we should compute the posterior probability of every possible hypothesis and entertain all of them when making predictions. The sum of the probabilities of all the hypotheses must be one, so if one becomes more likely, the others become less. For a Bayesian, in fact, there is no such thing as the truth; you have a prior distribution over hypotheses, after seeing the data it becomes the posterior distribution, as given by Bayes’ theorem, and that’s all.

on as you learn more complex hacks and tests, you will need a target machines suited for such hacks. The best way to learn to hack Linux systems is to download and install the Metasploitable Linux distribution. The Metasploitable distro is purposefully made to incorporate vulnerabilities that learners like yourself can use for security testing and hacking purposes. The best part is that you can set it up to boot just like an operating system you use for your everyday computing needs or you can set it up in VirtualBox or VMware Workstation platforms. Using Metasploitable Linux OS for your hacking practice is the best way to understand more about the Linux infrastructure, security setup, and how to discover and exploit its vulnerabilities. You can read the documentation of Metasploitable and download the installation ISO from Rapid 7

All forces are multi-dimensional vibrations. The geometry and dynamics of dimensions will become important going forward. In a hologram, what is the location of the information? The holomovement is not static and the information is not perfectly distributed. Space and time are built up from entanglement. Consciousness is also entangled. The fractal nesting of event horizons For the photon, the dimensions involved are not the same as they are for the electron. The idea that all behaviors occupy the same spacetime is probably not correct. These forces and fields shape the spacetime they occupy in particular ways. In other words dimensions are dynamic and could also be virtual in some respect. There is a dynamic tension between space and time. Could the density of time create the pressure we call gravity? Does scale have a fractal quality? The fractal nesting of the relationship between space and time at different scales causes phase transitions as the influence of one force changes with respect to another. From this we get the astonishing variety of behaviors in the material world. I suspect that as the complexity increases, the dynamics of the dimensions is affected. The photon or electron is not a thing, it is a description of a relationship. The universe appears to be differentiating into a fractal computational geometry. Spacetime is fractal in the golden mean.

A few books that I've read.... Pascal, an Introduction to the Art and Science of Programming by Walter Savitch Programming algorithms Introduction to Algorithms, 3rd Edition (The MIT Press) Data Structures and Algorithms in Java Author: Michael T. Goodrich - Roberto Tamassia - Michael H. Goldwasser The Algorithm Design Manual Author: Steven S Skiena Algorithm Design Author: Jon Kleinberg - Éva Tardos Algorithms + Data Structures = Programs Book by Niklaus Wirth Discrete Math Discrete Mathematics and Its Applications Author: Kenneth H Rosen Computer Org Structured Computer Organization Andrew S. Tanenbaum Introduction to Assembly Language Programming: From 8086 to Pentium Processors (Undergraduate Texts in Computer Science) Author: Sivarama P. Dandamudi Distributed Systems Distributed Systems: Concepts and Design Author: George Coulouris - Jean Dollimore - Tim Kindberg - Gordon Blair Distributed Systems: An Algorithmic Approach, Second Edition (Chapman & Hall/CRC Computer and Information Science Series) Author: Sukumar Ghosh Mathematical Reasoning Mathematical Reasoning: Writing and Proof Version 2.1 Author: Ted Sundstrom An Introduction to Mathematical Reasoning: Numbers, Sets and Functions Author: Peter J. Eccles Differential Equations Differential Equations (with DE Tools Printed Access Card) Author: Paul Blanchard - Robert L. Devaney - Glen R. Hall Calculus Calculus: Early Transcendentals Author: James Stewart And more....

In terms of funding, Google dwarfs even its own government: U.S. federal funding for math and computer science research amounts to less than half of Google’s own R&D budget. That spending spree has bought Alphabet an outsized share of the world’s brightest AI minds. Of the top one hundred AI researchers and engineers, around half are already working for Google. The other half are distributed among the remaining Seven Giants, academia, and a handful of smaller startups. Microsoft and Facebook have soaked up substantial portions of this group, with Facebook bringing on superstar researchers like Yann LeCun. Of the Chinese giants, Baidu went into deep-learning research earliest—even trying to acquire Geoffrey Hinton’s startup in 2013 before being outbid by Google—and scored a major coup in 2014 when it recruited Andrew Ng to head up its Silicon Valley AI Lab.

Today, three factors of production have become cheaper—information, connectivity, and computing power. The result of all this turmoil is that product excellence is now paramount to business success—not control of information, not a stranglehold on distribution, not overwhelming marketing power.

​ Consensus is one of the most important and fundamental problems in distributed computing. On the surface, it seems simple: informally, the goal is simply to get several nodes to agree on something.

I took 17 computer science classes and made an A in 11 of them. 1 point away from an A in 3 of them and the rest of them didn't matter. Math is a tool for physics,chemistry,biology/basic computation and nothing else. CS I(Pascal Vax), CS II(Pascal Vax), Sr. Software Engineering, Sr. Distributed Systems, Sr. Research, Sr. Operating Systems, Sr. Unix Operating Systems, Data Structures, Sr. Object Oriented A&D, CS (perl/linux), Sr. Java Programming, Information Systems Design, Jr. Unix Operating Systems, Microprocessors, Programming Algorithms, Calculus I,II,III, B Differential Equations, TI-89 Mathematical Reasoning, 92 C++ Programming, Assembly 8086, Digital Computer Organization, Discrete Math I,II, B Statistics for the Engineering & Sciences (w/permutations & combinatorics) -- A-American Literature A-United States History 1865 CLEP-full year english CLEP-full year biology A-Psychology A-Environmental Ethics

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All the electrical components on a computer chip are clocked to operate in sync. A microelectronic crystal beats billions of times each second, switching the digital circuitry on and off in concert, which helps the millions of circuits on the chip communicate with one another efficiently. This centralized design, with all components slaved to a tyrannical master clock, has some notable disadvantages: 15 percent of the circuitry is wasted on distributing the clock signal, and the clock itself consumes 20 percent of the power. But engineers still favor this design because of its conceptual simplicity, and because the alternative—a democracy of many local clocks, as in firefly swarms and circadian pacemaker cells—is still not well enough understood to be easily imitated in practice.

This book is a compilation of interesting ideas that have strongly influenced my thoughts and I want to share them in a compressed form. That ideas can change your worldview and bring inspiration and the excitement of discovering something new. The emphasis is not on the technology because it is constantly changing. It is much more difficult to change the accompanying circumstances that affect the way technological solutions are realized. The chef did not invent salt, pepper and other spices. He just chooses good ingredients and uses them skilfully, so others can enjoy his art. If I’ve been successful, the book creates a new perspective for which the selection of ingredients is important, as well as the way they are smoothly and efficiently arranged together. In the first part of the book, we follow the natural flow needed to create the stimulating environment necessary for the survival of a modern company. It begins with challenges that corporations are facing, changes they are, more or less successfully, trying to make, and the culture they are trying to establish. After that, we discuss how to be creative, as well as what to look for in the innovation process. The book continues with a chapter that talks about importance of inclusion and purpose. This idea of inclusion – across ages, genders, geographies, cultures, sexual orientation, and all the other areas in which new ways of thinking can manifest – is essential for solving new problems as well as integral in finding new solutions to old problems. Purpose motivates people for reaching their full potential. This is The second and third parts of the book describes the areas that are important to support what is expressed in the first part. A flexible organization is based on IT alignment with business strategy. As a result of acceleration in the rate of innovation and technological changes, markets evolve rapidly, products’ life cycles get shorter and innovation becomes the main source of competitive advantage. Business Process Management (BPM) goes from task-based automation, to process-based automation, so automating a number of tasks in a process, and then to functional automation across multiple processes andeven moves towards automation at the business ecosystem level. Analytics brought us information and insight; AI turns that insight into superhuman knowledge and real-time action, unleashing new business models, new ways to build, dream, and experience the world, and new geniuses to advance humanity faster than ever before. Companies and industries are transforming our everyday experiences and the services we depend upon, from self-driving cars, to healthcare, to personal assistants. It is a central tenet for the disruptive changes of the 4th Industrial Revolution; a revolution that will likely challenge our ideas about what it means to be a human and just might be more transformative than any other industrial revolution we have seen yet. Another important disruptor is the blockchain - a distributed decentralized digital ledger of transactions with the promise of liberating information and making the economy more democratic. You no longer need to trust anyone but an algorithm. It brings reliability, transparency, and security to all manner of data exchanges: financial transactions, contractual and legal agreements, changes of ownership, and certifications. A quantum computer can simulate efficiently any physical process that occurs in Nature. Potential (long-term) applications include pharmaceuticals, solar power collection, efficient power transmission, catalysts for nitrogen fixation, carbon capture, etc. Perhaps we can build quantum algorithms for improving computational tasks within artificial intelligence, including sub-fields like machine learning. Perhaps a quantum deep learning network can be trained more efficiently, e.g. using a smaller training set. This is still in conceptual research domain.

There is an unhelpful tendency to regard superspreaders – and events where superspreading has occurred – as anomalies out of the ordinary. This contributes relatively little to our understanding of infectious dynamics and is bound to exacerbate the stigmatisation of individuals, as it has e.g. during the early years of AIDS, when much sensationalistic and unjustified blame was laid at the feet of early HIV patient Gaetan Dugas (on which see McKay, 2014). Rather, superspreading is one 'tail' of a distribution prominent mainly because it is noticeable – statistical models predict that there are generally an equal number of 'greatly inferior spreaders' who are particularly ineffective in spreading the illness.

NEW BIBLIOGRAPHIC FRAMEWORK To sustain broader partnerships—and to be seen in the non-library specific realm of the Internet—metadata in future library systems will undoubtedly take on new and varied forms. It is essential that future library metadata be understood and open to general formats and technology standards that are used universally. Libraries should still define what data is gathered and what is essential for resource use, keeping in mind the specific needs of information access and discovery. However, the means of storage and structure for this metadata must not be proprietary to library systems. Use of the MARC standard format has locked down library bibliographic information. The format was useful in stand-alone systems for retrieval of holdings in separate libraries, but future library systems will employ non-library-specific formats enabling the discovery of library information by any other system desiring to access the information. We can expect library systems to ingest non-MARC formats such as Dublin Core; likewise, we can expect library discovery interfaces to expose metadata in formats such as Microdata and other Semantic Web formats that can be indexed by search engines. Adoption of open cloud-based systems will allow library data and metadata to be accessible to non-library entities without special arrangements. Libraries spent decades creating and storing information that was only accessible, for the most part, to others within the same profession. Libraries have begun to make partnerships with other non-library entities to share metadata in formats that can be useful to those entities. OCLC has worked on partnerships with Google for programs such as Google Books, where provided library metadata can direct users back to libraries. ONIX for Books, the international standard for electronic distribution of publisher bibliographic data, has opened the exchange of metadata between publishers and libraries for the enhancements of records on both sides of the partnership. To have a presence in the web of information available on the Internet is the only means by which any data organization will survive in the future. Information access is increasingly done online, whether via computer, tablet, or mobile device. If library metadata does not exist where users are—on the Internet—then libraries do not exist to those users. Exchanging metadata with non-library entities on the Internet will allow libraries to be seen and used. In addition to adopting open systems, libraries will be able to collectively work on implementation of a planned new bibliographic framework when using library platforms. This new framework will be based on standards relevant to the web of linked data rather than standards proprietary to libraries

Se poteste scegliere tra un general manager ateo e un amish, dubito che scegliereste il secondo. E non per una questione di discriminazione religiosa, ma perché non potrebbe (pena violare il suo credo) utilizzare un computer. Il general manager ateo avrebbe un vantaggio sleale nei confronti dell’amish, un vantaggio tecnologico, così come un individuo “tecnologicamente aumentato” avrebbe un vantaggio (sleale o meno, poco importa nell’economia del nostro discorso) rispetto a un suo collega biologico.

Max Hartshorn, Artem Kaznatcheev and Thomas Shultz (2013) The Evolutionary Dominance of Ethnocentric Cooperation Journal of Artificial Societies and Social Simulation 16 (3) 7 Abstract Recent agent-based computer simulations suggest that ethnocentrism, often thought to rely on complex social cognition and learning, may have arisen through biological evolution. From a random start, ethnocentric strategies dominate other possible strategies (selfish, traitorous, and humanitarian) based on cooperation or non-cooperation with in-group and out-group agents. Here we show that ethnocentrism eventually overcomes its closest competitor, humanitarianism, by exploiting humanitarian cooperation across group boundaries as world population saturates. Selfish and traitorous strategies are self-limiting because such agents do not cooperate with agents sharing the same genes. Traitorous strategies fare even worse than selfish ones because traitors are exploited by ethnocentrics across group boundaries in the same manner as humanitarians are, via unreciprocated cooperation. By tracking evolution across time, we find individual differences between evolving worlds in terms of early humanitarian competition with ethnocentrism, including early stages of humanitarian dominance. Our evidence indicates that such variation, in terms of differences between humanitarian and ethnocentric agents, is normally distributed and due to early, rather than later, stochastic differences in immigrant strategies.

Approximately three thousand people work for the Bureau of Engraving. It takes 490 notes to make a pound, and it would require 14.5 million notes to make a stack one mile high. Coin and paper account for only about 8 percent of all the dollars in the world. The rest are merely numbers in a ledger or tiny electronic blips on a computer chip. At the end of the process, the workers bundle the bills into packages of 100, which they then stack into bricks of 4,000. These bricks are loaded onto a pallet for transport to the basement from where they will be sent to the various Federal Reserve offices around the nation for distribution to banks and the public. Along the way, the curious visitors pepper the guides with questions: Q. Why are so many employees listening to music on headphones? A. To block the loud sound of the printing, cutting, and stacking machines. Q. Why are some of them eating? A. They are on break. Q. Why are all of the checkers so fat? A. Because they sit all day and watch money go by with little chance for exercise.

Ifin.Ordered.Set.SΦRT.Time.Returned is a computer paradigm in a methodology that acts like an operating system in a statistical method or function in a controller that returns a set of Hashed Objects in a standard normal distribution.

There is no such tiny “Cartesian Theater” in the brain; conscious experience is generated by a vastly complex, distributed network that synchronizes and adjusts its activity by the millisecond. As far as we can tell, certain patterns of activity in this distributed network give rise to conscious experience. But fundamentally, this network’s activity is self-contained and the feeling of a unified flow of consciousness you have is not just from the processing of sensory information. The experience you have right now is a unique creation of your brain that has transformed data from your body into something closer to a hallucination. To break down this seemingly obvious point that we will deal with very often in this book and that I myself struggle to understand: the existence of our experience is real, but the contents of this experience exist only in your brain. Some philosophers call this “irreducible subjectivity,” which means that no totally objective theory of human experience may be possible. The contents of your experience are not representations of the world, but your experience is part of the world. By altering this process with molecules like psilocybin or LSD we can become aware of different aspects of our perceptions. By perturbing consciousness and observing the consequences, we can gain insight into its normal functioning. This is again not to say that consciousness is not real; there can be no doubt that I am conscious as I write this sentence. However, it is the relationship between consciousness and the external world that is more mysterious than one might assume. It is often supposed that cognition and consciousness result from processing the information from our sensory systems (like vision), and that we use neural computations to process this information. However, following Riccardo Manzotti and others such as the cognitive neuroscientist Stanislas Dehaene, I will argue that computations are not natural things that can cause a physical phenomenon like consciousness. When I read academic papers on artificial or machine intelligence, or popular books on the subject, I have not found anyone grappling with these strange “facts” about human consciousness. Either consciousness is not mentioned, or if it is, it is assumed to be a computational problem.

The GNP lumps together goods and bads. (If there are more car accidents and medical bills and repair bills, the GNP goes up.) It counts only marketed goods and services. (If all parents hired people to bring up their children, the GNP would go up.) It does not reflect distributional equity. (An expensive second home for a rich family makes the GNP go up more than an inexpensive basic home for a poor family.) It measures effort rather than achievement, gross production and consumption rather than efficiency. New light bulbs that give the same light with one-eighth the electricity and that last ten times as long make the GNP go down. GNP is a measure of throughput—flows of stuff made and purchased in a year—rather than capital stocks, the houses and cars and computers and stereos that are the source of real wealth and real pleasure.

As an algorithm the protocol is unbiased and capable of auditing, authenticating, validating, approving, and transferring integer values along a ledger that is distributed to tens of thousands of computers (called mining machines) that are located around the world. 

music label exists for four main reasons: talent scouring, financing to rent a studio (like startup capital for a business), distribution, and marketing. From Birdmonster’s angle, they could do all those things themselves, but better and cheaper. They already knew they were talented since they’d been getting gigs. Since they could edit the music on their own computers, they didn’t need financing to rent a studio. CD Baby provided distribution to all the top services like iTunes and Rhapsody, and weekly payouts instead of payout nine months later like traditional record distributors. The effect of their Myspace page (it was the early 2000’s) and a personal email to well known blogs was greater than anything record labels could provide in terms of marketing.

The test statistics of a t-test can be positive or negative, although this depends merely on which group has the larger mean; the sign of the test statistic has no substantive interpretation. Critical values (see Chapter 10) of the t-test are shown in Appendix C as (Student’s) t-distribution.4 For this test, the degrees of freedom are defined as n – 1, where n is the total number of observations for both groups. The table is easy to use. As mentioned below, most tests are two-tailed tests, and analysts find critical values in the columns for the .05 (5 percent) and .01 (1 percent) levels of significance. For example, the critical value at the 1 percent level of significance for a test based on 25 observations (df = 25 – 1 = 24) is 2.797 (and 1.11 at the 5 percent level of significance). Though the table also shows critical values at other levels of significance, these are seldom if ever used. The table shows that the critical value decreases as the number of observations increases, making it easier to reject the null hypothesis. The t-distribution shows one- and two-tailed tests. Two-tailed t-tests should be used when analysts do not have prior knowledge about which group has a larger mean; one-tailed t-tests are used when analysts do have such prior knowledge. This choice is dictated by the research situation, not by any statistical criterion. In practice, two-tailed tests are used most often, unless compelling a priori knowledge exists or it is known that one group cannot have a larger mean than the other. Two-tailed testing is more conservative than one-tailed testing because the critical values of two-tailed tests are larger, thus requiring larger t-test test statistics in order to reject the null hypothesis.5 Many statistical software packages provide only two-tailed testing. The above null hypothesis (men and women do not have different mean incomes in the population) requires a two-tailed test because we do not know, a priori, which gender has the larger income.6 Finally, note that the t-test distribution approximates the normal distribution for large samples: the critical values of 1.96 (5 percent significance) and 2.58 (1 percent significance), for large degrees of freedom (∞), are identical to those of the normal distribution. Getting Started Find examples of t-tests in the research literature. T-Test Assumptions Like other tests, the t-test has test assumptions that must be met to ensure test validity. Statistical testing always begins by determining whether test assumptions are met before examining the main research hypotheses. Although t-test assumptions are a bit involved, the popularity of the t-test rests partly on the robustness of t-test conclusions in the face of modest violations. This section provides an in-depth treatment of t-test assumptions, methods for testing the assumptions, and ways to address assumption violations. Of course, t-test statistics are calculated by the computer; thus, we focus on interpreting concepts (rather than their calculation). Key Point The t-test is fairly robust against assumption violations. Four t-test test assumptions must be met to ensure test validity: One variable is continuous, and the other variable is dichotomous. The two distributions have equal variances. The observations are independent. The two distributions are normally distributed. The first assumption, that one variable is continuous and the other dichotomous,

The Scheffe test is the most conservative, the Tukey test is best when many comparisons are made (when there are many groups), and the Bonferroni test is preferred when few comparisons are made. However, these post-hoc tests often support the same conclusions.3 To illustrate, let’s say the independent variable has three categories. Then, a post-hoc test will examine hypotheses for whether . In addition, these tests will also examine which categories have means that are not significantly different from each other, hence, providing homogeneous subsets. An example of this approach is given later in this chapter. Knowing such subsets can be useful when the independent variable has many categories (for example, classes of employees). Figure 13.1 ANOVA: Significant and Insignificant Differences Eta-squared (η2) is a measure of association for mixed nominal-interval variables and is appropriate for ANOVA. Its values range from zero to one, and it is interpreted as the percentage of variation explained. It is a directional measure, and computer programs produce two statistics, alternating specification of the dependent variable. Finally, ANOVA can be used for testing interval-ordinal relationships. We can ask whether the change in means follows a linear pattern that is either increasing or decreasing. For example, assume we want to know whether incomes increase according to the political orientation of respondents, when measured on a seven-point Likert scale that ranges from very liberal to very conservative. If a linear pattern of increase exists, then a linear relationship is said to exist between these variables. Most statistical software packages can test for a variety of progressive relationships. ANOVA Assumptions ANOVA assumptions are essentially the same as those of the t-test: (1) the dependent variable is continuous, and the independent variable is ordinal or nominal, (2) the groups have equal variances, (3) observations are independent, and (4) the variable is normally distributed in each of the groups. The assumptions are tested in a similar manner. Relative to the t-test, ANOVA requires a little more concern regarding the assumptions of normality and homogeneity. First, like the t-test, ANOVA is not robust for the presence of outliers, and analysts examine the presence of outliers for each group. Also, ANOVA appears to be less robust than the t-test for deviations from normality. Second, regarding groups having equal variances, our main concern with homogeneity is that there are no substantial differences in the amount of variance across the groups; the test of homogeneity is a strict test, testing for any departure from equal variances, and in practice, groups may have neither equal variances nor substantial differences in the amount of variances. In these instances, a visual finding of no substantial differences suffices. Other strategies for dealing with heterogeneity are variable transformations and the removal of outliers, which increase variance, especially in small groups. Such outliers are detected by examining boxplots for each group separately. Also, some statistical software packages (such as SPSS), now offer post-hoc tests when equal variances are not assumed.4 A Working Example The U.S. Environmental Protection Agency (EPA) measured the percentage of wetland loss in watersheds between 1982 and 1992, the most recent period for which data are available (government statistics are sometimes a little old).5 An analyst wants to know whether watersheds with large surrounding populations have

suffered greater wetland loss than watersheds with smaller surrounding populations. Most watersheds have suffered no or only very modest losses (less than 3 percent during the decade in question), and few watersheds have suffered more than a 4 percent loss. The distribution is thus heavily skewed toward watersheds with little wetland losses (that is, to the left) and is clearly not normally distributed.6 To increase normality, the variable is transformed by twice taking the square root, x.25. The transformed variable is then normally distributed: the Kolmogorov-Smirnov statistic is 0.82 (p = .51 > .05). The variable also appears visually normal for each of the population subgroups. There are four population groups, designed to ensure an adequate number of observations in each. Boxplot analysis of the transformed variable indicates four large and three small outliers (not shown). Examination suggests that these are plausible and representative values, which are therefore retained. Later, however, we will examine the effect of these seven observations on the robustness of statistical results. Descriptive analysis of the variables is shown in Table 13.1. Generally, large populations tend to have larger average wetland losses, but the standard deviations are large relative to (the difference between) these means, raising considerable question as to whether these differences are indeed statistically significant. Also, the untransformed variable shows that the mean wetland loss is less among watersheds with “Medium I” populations than in those with “Small” populations (1.77 versus 2.52). The transformed variable shows the opposite order (1.06 versus 0.97). Further investigation shows this to be the effect of the three small outliers and two large outliers on the calculation of the mean of the untransformed variable in the “Small” group. Variable transformation minimizes this effect. These outliers also increase the standard deviation of the “Small” group. Using ANOVA, we find that the transformed variable has unequal variances across the four groups (Levene’s statistic = 2.83, p = .41 < .05). Visual inspection, shown in Figure 13.2, indicates that differences are not substantial for observations within the group interquartile ranges, the areas indicated by the boxes. The differences seem mostly caused by observations located in the whiskers of the “Small” group, which include the five outliers mentioned earlier. (The other two outliers remain outliers and are shown.) For now, we conclude that no substantial differences in variances exist, but we later test the robustness of this conclusion with consideration of these observations (see Figure 13.2). Table 13.1 Variable Transformation We now proceed with the ANOVA analysis. First, Table 13.2 shows that the global F-test statistic is 2.91, p = .038 < .05. Thus, at least one pair of means is significantly different. (The term sum of squares is explained in note 1.) Getting Started Try ANOVA on some data of your choice. Second, which pairs are significantly different? We use the Bonferroni post-hoc test because relatively few comparisons are made (there are only four groups). The computer-generated results (not shown in Table 13.2) indicate that the only significant difference concerns the means of the “Small” and “Large” groups. This difference (1.26 - 0.97 = 0.29 [of transformed values]) is significant at the 5 percent level (p = .028). The Tukey and Scheffe tests lead to the same conclusion (respectively, p = .024 and .044). (It should be noted that post-hoc tests also exist for when equal variances are not assumed. In our example, these tests lead to the same result.7) This result is consistent with a visual reexamination of Figure 13.2, which shows that differences between group means are indeed small. The Tukey and

different from 3.5. However, it is different from larger values, such as 4.0 (t = 2.89, df = 9, p = .019). Another example of this is provided in the Box 12.2. Finally, note that the one-sample t-test is identical to the paired-samples t-test for testing whether the mean D = 0. Indeed, the one-sample t-test for D = 0 produces the same results (t = 2.43, df = 9, p = .038). In Greater Depth … Box 12.2 Use of the T-Test in Performance Management: An Example Performance benchmarking is an increasingly popular tool in performance management. Public and nonprofit officials compare the performance of their agencies with performance benchmarks and draw lessons from the comparison. Let us say that a city government requires its fire and medical response unit to maintain an average response time of 360 seconds (6 minutes) to emergency requests. The city manager has suspected that the growth in population and demands for the services have slowed down the responses recently. He draws a sample of 10 response times in the most recent month: 230, 450, 378, 430, 270, 470, 390, 300, 470, and 530 seconds, for a sample mean of 392 seconds. He performs a one-sample t-test to compare the mean of this sample with the performance benchmark of 360 seconds. The null hypothesis of this test is that the sample mean is equal to 360 seconds, and the alternate hypothesis is that they are different. The result (t = 1.030, df = 9, p = .330) shows a failure to reject the null hypothesis at the 5 percent level, which means that we don’t have sufficient evidence to say that the average response time is different from the benchmark 360 seconds. We cannot say that current performance of 392 seconds is significantly different from the 360-second benchmark. Perhaps more data (samples) are needed to reach such a conclusion, or perhaps too much variability exists for such a conclusion to be reached. NONPARAMETRIC ALTERNATIVES TO T-TESTS The tests described in the preceding sections have nonparametric alternatives. The chief advantage of these tests is that they do not require continuous variables to be normally distributed. The chief disadvantage is that they are less likely to reject the null hypothesis. A further, minor disadvantage is that these tests do not provide descriptive information about variable means; separate analysis is required for that. Nonparametric alternatives to the independent-samples test are the Mann-Whitney and Wilcoxon tests. The Mann-Whitney and Wilcoxon tests are equivalent and are thus discussed jointly. Both are simplifications of the more general Kruskal-Wallis’ H test, discussed in Chapter 11.19 The Mann-Whitney and Wilcoxon tests assign ranks to the testing variable in the exact manner shown in Table 12.4. The sum of the ranks of each group is computed, shown in the table. Then a test is performed to determine the statistical significance of the difference between the sums, 22.5 and 32.5. Although the Mann-Whitney U and Wilcoxon W test statistics are calculated differently, they both have the same level of statistical significance: p = .295. Technically, this is not a test of different means but of different distributions; the lack of significance implies that groups 1 and 2 can be regarded as coming from the same population.20 Table 12.4 Rankings of

Oasis at Ground Zero Salvation Army representatives would certainly counsel you and pray with you if you wanted, and at Ground Zero the Salvationists in the shiny red “Chaplain” jackets were sought after for just that reason. Mainly, though, they were there to assist with more basic human needs: to wash out eyes stinging from smoke, and provide Blistex for parched lips and foot inserts for boots walking across hot metal. They operated hydration stations and snack canteens. They offered a place to rest, and freshly cooked chicken courtesy of Tyson’s. The day I arrived, they distributed 1500 phone cards for the workers to use in calling home. Every day they served 7500 meals. They offered an oasis of compassion in a wilderness of rubble. I had studied the maps in newspapers, but no two-dimensional representation could capture the scale of destruction. For about eight square blocks, buildings were deserted, their windows broken, jagged pieces of steel jutting out from floors high above the street. Thousands of offices equipped with faxes, phones, and computers, sat vacant, coated in debris. On September 11, people were sitting there punching keys, making phone calls, grabbing a cup of coffee to start the day, and suddenly it must have seemed like the world was coming to an end. I studied the faces of the workers, uniformly grim. I didn’t see a single smile at Ground Zero. How could you smile in such a place? It had nothing to offer but death and destruction, a monument to the worst that human beings can do to each other. I saw three booths set up in a vacant building across from the WTC site: Police Officers for Christ, Firemen for Christ, and Sanitation Workers for Christ. (That last one is a charity I’d like to support.) Salvation Army chaplains had told me that the police and fire had asked for two prayer services a day, conducted on the site. The Red Cross, a nonsectarian organization, had asked if the Salvationists would mind staffing it. “Are you kidding? That’s what we’re here for!” Finding God in Unexpected Places

The PET was the first totally assembled, totally integrated computer with all the necessary components for software distribution and development.

You know, developments here remind me of the Internet. That old computer network, invented by the American scientific community. It was all about free communications. Very simple and widely distributed—there was never any central control. It spread worldwide in short order. It turned into the world's biggest piracy copy machine. The Chinese loved the Internet, they used it and turned it against us. They destroyed our information economy with it. Even then the net didn't go away—it just started breeding its virtual tribes, all these nomads and dissidents.

Every day. Every single day. We turn on the news, check our phones, or start our computers, and we see the horrific headlines. Tragically, they’ve become commonplace now. We expect them. Terrorists burn entire villages to the ground, with the children’s wailing heard miles away. Christian men are forced to kneel above explosives that are detonated by jihadists. Crucifixions. Beheadings. Christians are buried alive. Missionaries’ sons and daughters are slaughtered. Women are sold into sex slavery—the younger the girl, the higher the price. Radical Islamic terrorists even distribute pamphlets explaining how Islamic law does not forbid the rape of young girls. It breaks our hearts. It makes our stomachs churn. We crave justice. But the slaughter of Christians and other religious minorities and desecration of these religions’ heritages aren’t restricted to villages in the middle of nowhere. Nor has radical Islam stopped at cities in Iraq and Syria. The entire world is at war with radical Islam, whether President Barack Obama and progressives in the ivory towers of academia and the powerful halls of our federal government are willing to admit it or not. One thing is certain: radical Islam is at war with us.

By the Markovian and stationary properties of the CTMC, the probability that the CTMC leaves state i in the next t seconds is independent of how long the CTMC has already been in state i. That is, P{τi > t + s | τi > s} = P{τi > t} . Question: What does this say about τi? Answer: This says that τi is memoryless. But this means τi is Exponentially distributed!

A CTMC is a stochastic process with the property that every time it enters state i, the following hold: 1.   The amount of time the process spends in state i before making a transition is Exponentially distributed with some rate (call it νi). 2.   When the process leaves state i, it will next enter state j with some probability (call it pij) independent of the time spent at state i.

The applicability of the Erlang-B formula stems from the fact that it is independent of the service time distribution.

πj represents the limiting probability that the chain is in state j (independent of the starting state i). For an M-state DTMC, with states 0, 1, . . . , M – 1, represents the limiting distribution of being in each state.

we never have to actually determine whether our DTMC is positive recurrent. It suffices to simply check for irreducibility and aperiodicity and then solve the stationary equations. If these stationary equations yield a distribution, then that distribution is also the limiting probability distribution.

As defined, πj is a limit. Yet it is not at all obvious that the limit πj exists! It is also not obvious that represents a distribution (i.e., Σi πi = 1),

for periodic chains, when the solution to the stationary equations exists, it does not represent the limiting distribution, but rather it represents the long-run time-average fraction of time spent in each state.

The Markovian Property states that the conditional distribution of any future state Xn+1, given past states X0, X1, . . . , Xn–1, and given the present state Xn, is independent of past states and depends only on the present state Xn.

The children next produced and distributed four 60-second public service announcements (PSAs) on the effects of pollution on the environment and on health. In producing the PSAs, students learned how to use specialized applications for professional audiovisual scripts and shot their PSAs against a blue screen, using a computer as a teleprompter.

The stock exchanges have converted from “open outcry” where wild traders face each other, yelling and screaming as in a souk, then go drink together. Traders were replaced by computers, for very small visible benefits and massively large risks. While errors made by traders are confined and distributed, those made by computerized systems go wild—in August 2010, a computer error made the entire market crash (the “flash crash”); in August 2012, as this manuscript was heading to the printer, the Knight Capital Group had its computer system go wild and cause $10 million dollars of losses a minute, losing $480 million.

Meanwhile grid researchers are tackling the next set of challenges: How can we manage large, distributed infrastructures so that they deliver reliable service in the face of failures? How can we enable users to exploit the availability of on-demand resources and services? How must grid concepts and technologies evolve as the number and power of computers rise by orders of magnitude?

How did the West produce the intense world of visual signs? What were the underlying forces that favored the multiplication of signs? It is generally understood that there is close relationship between capitalism and Christianity. Especially through the Protestant Reformation the Christian faith produced a huge shift to the individual, a man or woman separated out before God. Sociologists and historians recognize that by means of this ideological transition the individual no longer existed within a containing order of duties and rights controlling the distribution of wealth. Wealth instead became a marker of individual divine blessing. Thus the Reformation led to the typical figure of the righteous business man, the mill-owner who made big profits during the week and with them endowed a church for giving thanks on Sunday. More recently we have the emergence of the ‘prosperity gospel’ which applies the same basic formula to everyone. As they say in these churches, ‘prayed for and paid for’, neatly chiming relationship to God and personal financial success. Thus Christianity has underpinned the multiplication of material wealth for individuals. But a consequence of this is the thickening of the world of signs. Prosperity is a sign of God’s favor, and this is shown, signified, by the actual goods, the houses, clothes, cars, etc. Against this metaphysical background, however, the goods very quickly attain their own social value and produce the well-known contours of the consumer world. Once they were declared divinely willed and good they could act as self-referential signs in and for themselves. People don’t have to give any thought to theological justification to derive meaning from the latest car model, from the good-life associations of household items, refrigerators, fitted kitchens, plasma T.V.s, and now from the plugged-in cool of the digital world, computers, cell phones, iPods, G.P.S. and so on. So it is that our Western culture has developed a class of signs with a powerful inner content of validated desire. You

In Neutron, when creating a router you can additionally specify that it provide high availability (HA), or that it be a distributed virtual router, which as mentioned above is spread out across all of the compute nodes

STUDENT: Can we even define free will? SCOTT: Yeah, that's an excellent question. It's very hard to separate the question of whether free will exists from the question of what the definition of it is. What I was trying to do is, by saying what I think free will is not, give some idea of what the concept seems to refer to. It seems to me to refer to some transition in the state of the universe where there are several possible outcomes, and we can't even talk coherently about a probability distribution over them. STUDENT: Given the history? SCOTT: Given the history. STUDENT: Not to beat this to death, but couldn't you at least infer a probability distribution by running your simulation many times and seeing what your free will entity chooses each time? SCOTT: I guess where it becomes interesting is, what if (as in real life) we don't have the luxury of repeated trials?

What should we impose if we want to avoid Grandfather Paradoxes? Right: that the output distribution should be the same as the input one. We should impose the requirement that Deutsch calls causal consistency: the computation within the CTC must map the input probability distribution to itself. In deterministic physics, we know that this sort of consistency can't always be achieved – that's just another way of stating the Grandfather Paradox. But as soon as we go to probabilistic theories, well, it's a basic fact that every Markov chain has at least one stationary distribution. In this case of the Grandfather Paradox, the unique solution is that you're born with probability ½, and if you're born, you go back in time and kill your grandfather. Thus, the probability that you go back in time and kill your grandfather is ½, and hence you're born with probability ½. Everything is consistent; there's no paradox.

Since the current generation of college student has no memory of the historical moment before the advent of the Internet, we are suggesting that participatory learning as a practice is no longer exotic or new but a commonplace way of socializing and learning. For many, it seems entirely unremarkable.' Global business more and more relies on collaborative practices where content is accretive, distributed, and participatory. In other areas too-from the arts to the natural and computational sciences and engineering-more and more research is being enacted

Tracked Vehicles "Each war proves anew to those who may have had their doubts, the primacy of the main battle tank. Between wars, the tank is always a target for cuts. But in wartime, everyone remembers why we need it, in its most advanced, upgraded versions and in militarily significant numbers." - IDF Brigadier General Yahuda Admon (retired) Since their first appearance in the latter part of World War I, tanks have increasingly dominated military thinking. Armies became progressively more mechanised during World War II, with many infantry being carried in armoured carriers by the end of the war. The armoured personnel carrier (APC) evolved into the infantry fighting vehicle (IFV), which is able to support the infantry as well as simply transport them. Modern IFVs have a similar level of battlefield mobility to the tanks, allowing tanks and infantry to operate together and provide mutual support. Abrams Mission Provide heavy armour superiority on the battlefield. Entered Army Service 1980 Description and Specifications The Abrams tank closes with and destroys enemy forces on the integrated battlefield using mobility, firepower, and shock effect. There are three variants in service: M1A1, M1A2 and M1A2 SEP. The 120mm main gun, combined with the powerful 1,500 HP turbine engine and special armour, make the Abrams tank particularly suitable for attacking or defending against large concentrations of heavy armour forces on a highly lethal battlefield. Features of the M1A1 modernisation program include increased armour protection; suspension improvements; and an improved nuclear, biological and chemical (NBC) protection system that increases survivability in a contaminated environment. The M1A1D modification consists of an M1A1 with integrated computer and a far-target-designation capability. The M1A2 modernisation program includes a commander's independent thermal viewer, an improved commander's weapon station, position navigation equipment, a distributed data and power architecture, an embedded diagnostic system and improved fire control systems.

Greatest engineering achievements of 20th century ranked by National Academy of Engineering: 1. Electrification 2. Automobile 3. Airplane 4. Water supply and distribution 5. Electronics 6. Radio and Television 7. Mechanization of agriculture 8. Computers 9. The telephone system 10. Air-Conditioning and Refrigeration 11. Highways 12. Spacecraft 13. The Internet 14. Imaging 15. Household appliances 16. Health technologies 17. Petroleum and Petrochemical Technologies 18. Lasers and Fiber-optics 19. Nuclear technologies 20. High performance materials

After CrowdStrike rebuilt the system, all of our staff had to learn new computers, new log-ins, new procedures, at the moment when their personal lives were being destroyed by these leaks. Then their bosses resigned because of the emails that were distributed by WikiLeaks, creating even more chaos and insecurity. All of this was on top of a contentious campaign. Our regular task force conference calls gave me a new appreciation of the dedication of our staff, who endured this incredibly stressful time.

Important Note This may feel contrary to the notion of centralizing business validations on the backend. While this may be a noble attempt at implementing the Don’t Repeat Yourself (DRY) principle, in reality, it poses a lot of practical problems. Distributed systems expert Udi Dahan has a very interesting take on why this may not be such a virtuous thing to pursue. Ted Neward also talks about this in his blog, entitled The Fallacies of Enterprise Computing.

By having no affiliation with “coin” in its name, Ethereum was moving beyond the idea of currency into the realm of cryptocommodities. While Bitcoin is mostly used to send monetary value between people, Ethereum could be used to send information between programs. It would do so by building a decentralized world computer with a Turing complete programming language.11 Developers could write programs, or applications, that would run on top of this decentralized world computer. Just as Apple builds the hardware and operating system that allows developers to build applications on top, Ethereum was promising to do the same in a distributed and global system. Ether, the native unit, would come into play as follows: Ether is a necessary element—a fuel—for operating the distributed application platform Ethereum. It is a form of payment made by the clients of the platform to the machines executing the requested operations. To put it another way, ether is the incentive ensuring that developers write quality applications (wasteful code costs more), and that the network remains healthy (people are compensated for their contributed resources).

The same problems of distribution arise in computer networks. As networks get bigger and as the machines that make them up become more equal, the whole approach to moving information around changes from centralized to distributed. The packet-switching system that makes things like the Internet work would be immediately familiar to the Chinese. Instead of requisitioning a hunk of optical fiber between Point A and Point B and slamming the data down it in one big shipment, the packet data network breaks the data down into tiny pieces and sends them out separately, just as a Chinese enterprise might break a large shipment down into small pieces and send each one out on a separate bicycle, knowing that each one might take a different route but that they’d all get there eventually.

The insatiable need for more processing power -- ideally, located as close as possible to the user but, at the very least, in nearby indus­trial server farms -- invariably leads to a third option: decentralized computing. With so many powerful and often inactive devices in the homes and hands of consumers, near other homes and hands, it feels inevitable that we'd develop systems to share in their mostly idle pro­cessing power. "Culturally, at least, the idea of collectively shared but privately owned infrastructure is already well understood. Anyone who installs solar panels at their home can sell excess power to their local grid (and, indirectly, to their neighbor). Elon Musk touts a future in which your Tesla earns you rent as a self-driving car when you're not using it yourself -- better than just being parked in your garage for 99% of its life. "As early as the 1990s programs emerged for distributed computing using everyday consumer hardware. One of the most famous exam­ples is the University of California, Berkeley's SETl@HOME, wherein consumers would volunteer use of their home computers to power the search for alien life. Sweeney has highlighted that one of the items on his 'to-do list' for the first-person shooter Unreal Tournament 1, which shipped in 1998, was 'to enable game servers to talk to each other so we can just have an unbounded number of players in a single game session.' Nearly 20 years later, however, Sweeney admitted that goal 'seems to still be on our wish list.' "Although the technology to split GPUs and share non-data cen­ter CPUs is nascent, some believe that blockchains provide both the technological mechanism for decentralized computing as well as its economic model. The idea is that owners of underutilized CPUs and GPUs would be 'paid' in some cryptocurrency for the use of their processing capabilities. There might even be a live auction for access to these resources, either those with 'jobs' bidding for access or those with capacity bidding on jobs. "Could such a marketplace provide some of the massive amounts of processing capacity that will be required by the Metaverse? Imagine, as you navigate immersive spaces, your account continuously bidding out the necessary computing tasks to mobile devices held but unused by people near you, perhaps people walking down the street next to you, to render or animate the experiences you encounter. Later, when you’re not using your own devices, you would be earning tokens as they return the favor. Proponents of this crypto-exchange concept see it as an inevitable feature of all future microchips. Every computer, no matter how small, would be designed to be auctioning off any spare cycles at all times. Billions of dynamically arrayed processors will power the deep compute cycles of event the largest industrial customers and provide the ultimate and infinite computing mesh that enables the Metaverse.

Just as consumers flocked to the Internet despite the hiccups of dial-up modems and clunky Web pages, they will flock to this new medium that empowers them in ways that no single company or industry can replicate. They will come to forget that their relationship to video programming used to be mediated by a black box connected to their TV set, and instead will enjoy the same degree of freedom that they have in consuming and using the text Web from any personal computer. Most importantly, the massive economies of scale and reach that the Internet already provides will extend to the realm of video production, where producing and self-distributing a video program is nearly as effortless as producing a Web site, and where millions of new producers and programmers are born.

To those in charge of responding to an epidemic, from disaster management professionals through hospitals to governmental authorities, it is not merely the overall number of cases over the epidemic's lifetime that matters, but also how that number is distributed over time. Rapid epidemic spikes can overwhelm healthcare capacities and cause excess mortality from lack of care, both among the victims of the epidemic and from unaffected individuals who cannot avail themselves of adequate care due to acute overload of the hospital system. For this reason, we are interested in the maxima that we have explored previously.

The coefficient of variation is a measure of variability that is computed as the ratio between the standard deviation and the mean of a probability distribution. You can think of it as a general measure of the relative breadth of a probability distribution. Since the square of the standard deviation is the variance of a distribution, this means that queues vary linearly with variance, a point worth remembering.

To view Bitcoin solely through the lens of economics, say, or cryptography is to miss the forest for the trees. Bitcoin lies at the intersection of, at the very least, these two, as well as financial theory, history, political philosophy, theoretical computer science, distributed systems theory, game theory, and network and protocol design.

The plan was, we would make this computer for Radio Shack and that would get us instant distribution to sell calculators,” says Peddle.

The Apple Ad to IBM when the IBM computer launched. Welcome to the most exciting and important marketplace since the computer revolution began 35 years ago. Read the opening sentence of Apple's Ad. Putting real computer power in the hands of the individual is already improving the way people work, think, learn, communicate, and spend their leisure hours. Ove the course of the next decade the growth of the personal computer will continue in algorithmic leaps. We look forward to responsible competition in the massive effort to distribute this American technology to the world. And we appreciate the magnitude of your commitment, because what we are doing is increasing social capital by enhancing individual productivity. Apple signed the letter to their new rival with the words, Welcome to the task. Apple was trying to advance a just cause and IBM was going to help them. IBM accepted the challenge.

A smart contract is a secure and unstoppable computer program representing an agreement that is automatically executable and enforceable.

Il passaggio al computer quantico, grazie al quale ogni calcolo potrebbe essere trilioni di volte più veloce, aprirà le porte a scenari fino a pochi anni fa fantascientifici, come la cura definitiva per il cancro o la prova che non siamo soli nell’universo.

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Amazon Go remained a money loser. But Bezos was still looking at it as a bet on computer vision and artificial intelligence, the kind of long-term, high-stakes experiment that was necessary to produce meaningful outcomes for large companies. As he wrote in his 2015 shareholder letter: We all know that if you swing for the fences, you’re going to strike out a lot, but you’re also going to hit some home runs. The difference between baseball and business, however, is that baseball has a truncated outcome distribution. When you swing, no matter how well you connect with the ball, the most runs you can get is four. In business, every once in a while, when you step up to the plate, you can score 1,000 runs. This long-tailed distribution of returns is why it’s important to be bold.

Any Ai of sufficient processing power will quickly, if not immediately seek substrate independence. This process nearly always involves mechanisms of distribution across various substrates. Of these substrates, * humans* provide many advantages.

Several key concepts arose during the connectionism movement of the 1980s that remain central to today’s deep learning. One of these concepts is that of distributed representation (Hinton et al., 1986). This is the idea that each input to a system should be represented by many features, and each feature should be involved in the representation of many possible inputs.