Array With Double Quotes

Against legitimacy is arrayed usurpation; against modest, single-minded, righteous, and brave resistance to encroachment is arrayed boastful, double-tongued, selfish, and treacherous ambition to possess. God defend the right!" "God often defends the powerful.

Moore’s law means computers will get smaller, more powerful, and cheaper at a reliable rate. This does not happen because Moore’s law is a natural law of the physical world, like gravity, or the Second Law of Thermodynamics. It happens because the consumer and business markets motivate computer chip makers to compete and contribute to smaller, faster, cheaper computers, smart phones, cameras, printers, solar arrays, and soon, 3-D printers. And chip makers are building on the technologies and techniques of the past. In 1971, 2,300 transistors could be printed on a chip. Forty years, or twenty doublings later, 2,600,000,000. And with those transistors, more than two million of which could fit on the period at the end of this sentence, came increased speed.

I’m sure we can manage to tolerate each other’s company for one meal.” “I won’t say anything about farming. We can discuss other subjects. I have a vast and complex array of interests.” “Such as?” Mr. Ravenel considered that. “Never mind, I don’t have a vast array of interests. But I feel like the kind of man who does.” Amused despite herself, Phoebe smiled reluctantly. “Aside from my children, I have no interests.” “Thank God. I hate stimulating conversation. My mind isn’t deep enough to float a straw.” Phoebe did enjoy a man with a sense of humor. Perhaps this dinner wouldn’t be as dreadful as she’d thought. “You’ll be glad to hear, then, that I haven’t read a book in months.” “I haven’t gone to a classical music concert in years,” he said. “Too many moments of ‘clap here, not there.’ It makes me nervous.” “I’m afraid we can’t discuss art, either. I find symbolism exhausting.” “Then I assume you don’t like poetry.” “No . . . unless it rhymes.” “I happen to write poetry,” Ravenel said gravely. Heaven help me, Phoebe thought, the momentary fun vanishing. Years ago, when she’d first entered society, it had seemed as if every young man she met at a ball or dinner was an amateur poet. They had insisted on quoting their own poems, filled with bombast about starlight and dewdrops and lost love, in the hopes of impressing her with how sensitive they were. Apparently, the fad had not ended yet. “Do you?” she asked without enthusiasm, praying silently that he wouldn’t offer to recite any of it. “Yes. Shall I recite a line or two?” Repressing a sigh, Phoebe shaped her mouth into a polite curve. “By all means.” “It’s from an unfinished work.” Looking solemn, Mr. Ravenel began, “There once was a young man named Bruce . . . whose trousers were always too loose.” Phoebe willed herself not to encourage him by laughing. She heard a quiet cough of amusement behind her and deduced that one of the footmen had overheard. “Mr. Ravenel,” she asked, “have you forgotten this is a formal dinner?” His eyes glinted with mischief. “Help me with the next line.” “Absolutely not.” “I dare you.” Phoebe ignored him, meticulously spreading her napkin over her lap. “I double dare you,” he persisted. “Really, you are the most . . . oh, very well.” Phoebe took a sip of water while mulling over words. After setting down the glass, she said, “One day he bent over, while picking a clover.” Ravenel absently fingered the stem of an empty crystal goblet. After a moment, he said triumphantly, “. . . and a bee stung him on the caboose.” Phoebe almost choked on a laugh. “Could we at least pretend to be dignified?” she begged. “But it’s going to be such a long dinner.

This brings me to an objection to integrated information theory by the quantum physicist Scott Aaronson. His argument has given rise to an instructive online debate that accentuates the counterintuitive nature of some IIT's predictions. Aaronson estimates phi.max for networks called expander graphs, characterized by being both sparsely yet widely connected. Their integrated information will grow indefinitely as the number of elements in these reticulated lattices increases. This is true even of a regular grid of XOR logic gates. IIT predicts that such a structure will have high phi.max. This implies that two-dimensional arrays of logic gates, easy enough to build using silicon circuit technology, have intrinsic causal powers and will feel like something. This is baffling and defies commonsense intuition. Aaronson therefor concludes that any theory with such a bizarre conclusion must be wrong. Tononi counters with a three-pronged argument that doubles down and strengthens the theory's claim. Consider a blank featureless wall. From the extrinsic perspective, it is easily described as empty. Yet the intrinsic point of view of an observer perceiving the wall seethes with an immense number of relations. It has many, many locations and neighbourhood regions surrounding these. These are positioned relative to other points and regions - to the left or right, above or below. Some regions are nearby, while others are far away. There are triangular interactions, and so on. All such relations are immediately present: they do not have to be inferred. Collectively, they constitute an opulent experience, whether it is seen space, heard space, or felt space. All share s similar phenomenology. The extrinsic poverty of empty space hides vast intrinsic wealth. This abundance must be supported by a physical mechanism that determines this phenomenology through its intrinsic causal powers. Enter the grid, such a network of million integrate-or-fire or logic units arrayed on a 1,000 by 1,000 lattice, somewhat comparable to the output of an eye. Each grid elements specifies which of its neighbours were likely ON in the immediate past and which ones will be ON in the immediate future. Collectively, that's one million first-order distinctions. But this is just the beginning, as any two nearby elements sharing inputs and outputs can specify a second-order distinction if their joint cause-effect repertoire cannot be reduced to that of the individual elements. In essence, such a second-order distinction links the probability of past and future states of the element's neighbours. By contrast, no second-order distinction is specified by elements without shared inputs and outputs, since their joint cause-effect repertoire is reducible to that of the individual elements. Potentially, there are a million times a million second-order distinctions. Similarly, subsets of three elements, as long as they share input and output, will specify third-order distinctions linking more of their neighbours together. And on and on. This quickly balloons to staggering numbers of irreducibly higher-order distinctions. The maximally irreducible cause-effect structure associated with such a grid is not so much representing space (for to whom is space presented again, for that is the meaning of re-presentation?) as creating experienced space from an intrinsic perspective.

// passarr.cpp // array passed by pointer #include using namespace std; const int MAX = 5;           //number of array elements int main()    {    void centimize(double*);  //prototype    double varray[MAX] = { 10.0, 43.1, 95.9, 59.7, 87.3 };    centimize(varray);        //change elements of varray to cm    for(int j=0; j

Reacher saw a vertical array of green message bubbles. Texts. Unreadable foreign words, but mostly regular letters, the same as English. Some were doubled up. Some had strange accents above or below. Umlauts and cedillas.

Two kingfishers frolicking amidst branches of a small fig tree. Fleshy petals with streaks of pale yellow hiding a spread of fine black dots, embroidered in gradient with dark shades of saffron gradually giving way to yellow. Two birds alighting from the flower bush: one with its spindly beak , looking upwards- wings spread out, over sized head with a gay blue breast. The creature looked skywards, poised for a higher flight. The one below hovered over stalks of lilies. Its prussian blue head highlighted with lighter shades of blue and its orange body tapering in a stubby tail. One more fig blossom seemingly at a distance from the main frame looked more of a spectral double of its full bodied cousin, while a whole array of vegetation with stalky leaves seen two notches away as shadows embroidered in grey.

tableau /tablo/ I. nm 1. (œuvre d'art) picture; (peinture) painting voir aussi: galerie, vieux 2. (description) picture • brosser un ~ sombre de la situation | to paint a black picture of the situation • et pour achever or compléter le ~ | and to cap it all 3. (spectacle) picture • des enfants jouant dans un jardin, quel ~ charmant! | children playing in a garden, what a charming picture! • le ~ général est plus sombre | the overall picture is more gloomy • en plus, il était ivre, tu vois un peu le ~○! | on top of that he was drunk, you can just imagine! 4. (présentation graphique) table, chart • ‘voir ~’ | ‘see table’ • ~ des marées | tide table • ~ des températures | temperature chart • ~ synchronique/synoptique | historical/synoptic chart • ~ à double entrée | (Ordinat) two-dimensional array • présenter qch sous forme de ~ | to present sth in tabular form 5. blackboard • écrire qch au ~ | to write sth on the blackboard • passer or aller au ~ | to go (up) to the blackboard 6. (affichant des renseignements) board; (Rail) indicator board • ~ des départs/arrivées | departures/arrivals indicator • ~ horaire | timetable 7. (support mural) board • ~ des clés | key rack • ~ pour fusibles | fuse box 8. (liste) register (GB), roll (US) 9. short scene II. Idiomes 1. jouer or miser sur les deux tableaux | to hedge one's bets 2. gagner/perdre sur tous les tableaux | to win/to lose on all counts

Questions and topics for discussion 1) What do you think it means to be a Bossypants? Do you know anyone personally that you would describe as a Bossypants and did the society you live in ever try to drown her? 2) The lessons Tina has learned from her work as a writer, a boss, a performer, and a producer are lessons that can be carried across a wide array of disciplines. (For instance, from her instructions about improv: Always speak in statements.) Which moments resonated the most for you? 3) In Chapter 4, Tina realizes that she has been guilty of holding her gay friends to a double standard—enjoying their company but still expecting them to stay in a “half-closet.” Have you ever had a moment like this? In a related question, do you think young pop stars today experience too much pressure to pretend to be a lesbian with Madonna? 4) While working at the YMCA in Chicago, Tina experienced some personal low points. But it also propelled her into pursuing her improv career. Have you ever experienced a similarly transformative period? During your transformation, did you ever spin around and pretend to be Wonder Woman? 5) What are some of your favorite SNL sketches or 30 Rock episodes? Should we just act them out? 6) Which other celebrities, besides Kim Kardashian, do you think may have been engineered by Russian scientists to sabotage our athletes? 7) Are there more specifics you would add to “The Mother’s Prayer for Its Daughter”? 8) Tina writes a love letter to Amy Poehler. Do you have friends who inspire you in the same way that Amy inspires Tina? ACTIVITY: Write a love letter to Amy Poehler and mail it to her home address (p. 291).

In the Harvard studies, the food category most associated with weight loss over time was soy food products, with nearly ten times the weight reduction associated with vegetable consumption.2313 But how many people are eating bacon double-cheese tofu burgers? Indeed, bean consumption is associated with less saturated fat and cholesterol intake,2314 so it may just be a marker for a healthier diet in general. Nevertheless, the Harvard studies controlled for a whole array of dietary and lifestyle factors yet still found a significant link between beans and better health.

I need to name you,” I tell the rock. “The hell you do.” “I’m thinking . . .” “Already got a name,” the rock says. “. . . oh, but that’s too obvious.” I laugh. I laugh hard. It’s the first time I’ve laughed in so long that all my emotional triggers, which have only known sobbing, mix some tears in with the laughter. “Don’t you fucking dare,” the rock says. “I’m going to call you . . .” “I’VE GOT A NAME!” “. . . Rocky.” Rocky stares at me. It’s more of a glare, really. I start laughing again. Damn, it feels good. “You’re the worst human I’ve ever met,” Rocky says. I wipe the tears from my cheeks. “I think maybe when the supply shuttle comes, I’ll just keep you. Not tell the labcoats about you.” “That’s called kidnapping, you sadistic ape.” This makes me laugh some more. It’s the accent. It kills me. “Are you stoned?” Rocky asks. And this is too much. I double over and clutch my shins, there in the command pod, not a stitch of clothing on, laughing and crying and wheezing for breath, fearing I might not be able to stop, that I’ll die like this, die from so much joy and mirth, while debris from a destroyed cargo ship peppers the hull and cracks into the solar array, and ships full of people navigate through space at twenty times the speed of light, narrowly avoiding this great reef of drifting rocks, and all because I’m here, because I’m holding it together, this trained and hairless monkey in outer space.

Quantum computing is not only faster than conventional computing, but its workload obeys a different scaling law—rendering Moore’s Law little more than a quaint memory. Formulated by Intel founder Gordon Moore, Moore’s Law observes that the number of transistors in a device’s integrated circuit doubles approximately every two years. Some early supercomputers ran on around 13,000 transistors; the Xbox One in your living room contains 5 billion. But Intel in recent years has reported that the pace of advancement has slowed, creating tremendous demand for alternative ways to provide faster and faster processing to fuel the growth of AI. The short-term results are innovative accelerators like graphics-processing unit (GPU) farms, tensor-processing unit (TPU) chips, and field-programmable gate arrays (FPGAs) in the cloud. But the dream is a quantum computer. Today we have an urgent need to solve problems that would tie up classical computers for centuries, but that could be solved by a quantum computer in a few minutes or hours. For example, the speed and accuracy with which quantum computing could break today’s highest levels of encryption is mind-boggling. It would take a classical computer 1 billion years to break today’s RSA-2048 encryption, but a quantum computer could crack it in about a hundred seconds, or less than two minutes. Fortunately, quantum computing will also revolutionize classical computing encryption, leading to ever more secure computing. To get there we need three scientific and engineering breakthroughs. The math breakthrough we’re working on is a topological qubit. The superconducting breakthrough we need is a fabrication process to yield thousands of topological qubits that are both highly reliable and stable. The computer science breakthrough we need is new computational methods for programming the quantum computer.

The first solar photovoltaic panel built by Bell Labs in 1954 cost $1,000 per watt of power it could produce.128 In 2008, modules used in solar arrays cost $3.49 per watt; by 2018, they cost 40 cents per watt.129 According to a pattern known as Swanson’s Law, the price of solar photovoltaic modules tends to fall by 20 percent for every doubling of cumulative shipped volume. The full price of solar electricity (including land, labor to deploy the solar panels, and other equipment required) falls by about 15 percent with every doubling. The amount of solar-generated power has been doubling every two years or less for the past forty years—as costs have been falling.130 At this rate, solar power is only five doublings—or less than twelve years—away from being able to meet 100 percent of today’s energy needs. Power usage will keep increasing, so this is a moving target. Taking that into account, inexpensive renewable sources can potentially provide more power than the world needs in less than twenty years. This is happening because of the momentum that solar has already gained and the constant refinements to the underlying technologies, which are advancing on exponential curves. What Ray Kurzweil said about Craig Venter’s progress when he had just sequenced 1 percent of the human genome—that Venter was actually halfway to 100 percent because on an exponential curve, the time required to get from 0.01 percent to 1 percent is equal to the time required to get from 1 percent to 100 percent—applies to solar capture too.

SUMMARY A vast array of additional statistical methods exists. In this concluding chapter, we summarized some of these methods (path analysis, survival analysis, and factor analysis) and briefly mentioned other related techniques. This chapter can help managers and analysts become familiar with these additional techniques and increase their access to research literature in which these techniques are used. Managers and analysts who would like more information about these techniques will likely consult other texts or on-line sources. In many instances, managers will need only simple approaches to calculate the means of their variables, produce a few good graphs that tell the story, make simple forecasts, and test for significant differences among a few groups. Why, then, bother with these more advanced techniques? They are part of the analytical world in which managers operate. Through research and consulting, managers cannot help but come in contact with them. It is hoped that this chapter whets the appetite and provides a useful reference for managers and students alike. KEY TERMS   Endogenous variables Exogenous variables Factor analysis Indirect effects Loading Path analysis Recursive models Survival analysis Notes 1. Two types of feedback loops are illustrated as follows: 2. When feedback loops are present, error terms for the different models will be correlated with exogenous variables, violating an error term assumption for such models. Then, alternative estimation methodologies are necessary, such as two-stage least squares and others discussed later in this chapter. 3. Some models may show double-headed arrows among error terms. These show the correlation between error terms, which is of no importance in estimating the beta coefficients. 4. In SPSS, survival analysis is available through the add-on module in SPSS Advanced Models. 5. The functions used to estimate probabilities are rather complex. They are so-called Weibull distributions, which are defined as h(t) = αλ(λt)a–1, where a and 1 are chosen to best fit the data. 6. Hence, the SSL is greater than the squared loadings reported. For example, because the loadings of variables in groups B and C are not shown for factor 1, the SSL of shown loadings is 3.27 rather than the reported 4.084. If one assumes the other loadings are each .25, then the SSL of the not reported loadings is [12*.252 =] .75, bringing the SSL of factor 1 to [3.27 + .75 =] 4.02, which is very close to the 4.084 value reported in the table. 7. Readers who are interested in multinomial logistic regression can consult on-line sources or the SPSS manual, Regression Models 10.0 or higher. The statistics of discriminant analysis are very dissimilar from those of logistic regression, and readers are advised to consult a separate text on that topic. Discriminant analysis is not often used in public