Yielded structures have a lower stiffness, leading to increased deflections and decreased buckling strength. The structure will be permanently deformed when the load is removed, and may have residual stresses. Engineering metals display strain hardening, which implies that the yield stress is increased after unloading from a yield state. Highly optimized structures, such as airplane beams and components, rely on yielding as a fail-safe failure mode. No safety factor is therefore needed when comparing limit loads (the highest loads expected during normal operation) to yield criteria. [ citation needed ]
One of the moral paradoxes that The Importance of Being Earnest seems intended to express is the idea that the perfectly moral man is the man who professes to be immoral, who speaks truly by virtue of the fact that he admits to being essentially a liar. Wilde set great store in lying, which, he argued in a quasi-Platonic dialogue called “The Decay of Lying,” is a veritable art form. Art itself may really be what’s at stake here. From Wilde’s standpoint, the poseur is to be congratulated and commended if his affectations bespeak elegance and style and achieve beauty. If they do, he is close to an artist. If they don’t, he is only a hypocrite.
While in principle that's possible, there are good practical reasons to use deep networks. As argued in Chapter 1 , deep networks have a hierarchical structure which makes them particularly well adapted to learn the hierarchies of knowledge that seem to be useful in solving real-world problems. Put more concretely, when attacking problems such as image recognition, it helps to use a system that understands not just individual pixels, but also increasingly more complex concepts: from edges to simple geometric shapes, all the way up through complex, multi-object scenes. In later chapters, we'll see evidence suggesting that deep networks do a better job than shallow networks at learning such hierarchies of knowledge. To sum up: universality tells us that neural networks can compute any function; and empirical evidence suggests that deep networks are the networks best adapted to learn the functions useful in solving many real-world problems.