logitech g27 in pcworld sale
Just ordered a g27 from pcworld (UK). Currently it is reduced to £169.99, so I figured a heads up was in order.
Out with the old, in with the new!
Also significant would be that when pressure waves in the exhaust reach transitions in diameter of the tube it causes a change in pressure, this results in some of the wave will be reflected back up the pipe. This also happens at the end of the pipe.
I agree. It's a system that promotes exploitation at every level.
I wouldn't call them "damn fools" though. The boom bust mechanic is a very efficient way for the elite to increase their wealth. So from their point of view, they are anything but fools! And why would they change what is for them a very profitable system. It's the ordinary folk who buy into the consumerism dogma who are fools IMO.
If income or growth begins to drop, shareholders start to dump stock, share value dives and you are left vulnerable to hostile takeover. Likely result is your company is bought out and asset stripped.
Another way to look at it is that the newer the windows version, the more security holes there are that have not yet been fixed by Microsoft.
If XP has had security fixes every week or so for 12 years, it is likely to be a whole lot more secure than windows 8.
It's all fine and dandy that Microsoft patches the more recent windows, but they can't do it instantly, so a new Windows is going to leak like a sieve for a few years compared to a more mature version, unless Microsoft find all the exploits before malicious hackers do.
Or maybe the hackers will move on to targeting new versions of windows, where they are likely to find more exploits more easily that they can milk for a few days until Microsoft patches them. They will also prefer to target an OS that users are unfamiliar with in the hope that they will be easier targets. In that case older OSes like XP are naturally going to be safer to use.
Of course this is pure conjecture just like your claims. Unless you can back them up with actual data?
You've done a lot of shouting and name calling, but provided no proof whatsoever. Lets see some actual data to back up your assertions. Show us some empirical evidence that XP is more dangerous right now than using the latest windows.
You only live once, why are you here using your precious time posting in the forum of software that you think is dead!
What a fool you must be
...and one massive chip on the shoulder does not make a tasty butty!
He's shaking up a can of coke for pastor (who's clearly not paying attention).
Or vectors!
You could rig up an ANN to give you vectors as well, you could theoretically rig up an ANN to solve most problems.
The issue here is that they are only _good_ at solving a limited set of problems.
There's no point in using Neural Nets in areas where they would be hopelessly inefficient or otherwise impractical. You would choose a more appropriate tool for the job.
Yes it is!
I clicked your 'let me google that for you' link and the second result includes various comments the back up my stance here. Both that ANNs are not good for modelling complex systems due to efficiency issues, and that you don't need to understand them to use them:
I've emboldened the pertinent sections.
Hmm, the crux of your argument seems to be calling me names. I was trying to ignore that and continue the tyre physics discussion. It's a shame you're intent on pursuing the ad hominem angle.
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Most important is an understanding of the non-trivial problem. e.g. If your roof is leaking, just going and hitting it with a hammer won't help. You need to understand how a roof works, and how slate works etc. When you have all that knowledge processed, you can use your hammer as a tool for fixing your roof - you still don't need to understand the physics of the hammer.
I got the point. And I accepted it. Then I pointed out that you would still need the heavy computation, you just moved it from time into space.
If the ball is not infinitely hard, then to model e.g. deformation and have the history encoded in the state, you need a finely grained FEA style model. Which to realize in an ANN would mean lots of nodes and lots and lots of connections.
And you need to run it very fast in order to model the high frequency behaviour of the system.
If you want to make a very simple model of a ball falling, and are happy to pretend your ball is an abstract, infinitely solid perfect sphere then that's fine. Unfortunately, that's not good enough for tyre physics in a racing sim.
I suppose there are various ways to go about it, but I don't see how you can ignore the fact that there is feedback in the system. If you want an ANN to model this without responding to the history of the system in some way, then you need an ANN with feedback and you need to divide the model up spatially with a higher granularity. You'll also need to run the sim at a much higher frequency.
Unfortunately, that doesn't solve the problem of the processing requirements being too heavy, it just moves the processing from one domain into another.
And as finite element analysis is a well explored technology, I doubt you'd get your spatially divided ANN version working as efficiently as Scawens existing FEA bench model if you went that route.
I'm not an expert in this area, so there must be alternatives that I'm not aware of.
Have you thought of a way to accurately model tyre physics using ANNs that can provide the high frequency feedback and dynamic nature of the system without finely grained input of some sort?
How would you reduce the complexity of the model without losing accuracy and realism?
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I consider a hammer to be a simple tool, and yet I would probably struggle with the math in a metallurgical analysis of the tempering process of steel.
Agreed - you put it better than me.
I think one of the main issues is that for a system like tyre physics, the NN cannot just look at the (significant number of) inputs at any single instance in time, it would need to process multiple sampling points on a time axis long enough to handle the 'impulse response' of the system being modelled, and at a high enough sampling frequency to represent the highest frequencies present in the system without aliasing. I would guess that if you take the number of time frames required, multiplied by the number of other inputs required for a detailed multi-dimentional model, you get a very big number. When you consider that each of these inputs must be connected to every single node of the ANNs hidden layer by a sigmoid function, then the processing requirements start to become astronomical.
Col
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I said they were simple tools. You can use simple tools to solve complex problems - pattern recognition for example.
I think ANN's are not a good solution for real-time tyre modelling on consumer computing devices!
And now I hope that you are going to explain in technical detail how it can be done, and I will learn something useful. Otherwise, stop blowing hot air
I assume from your response that you have solved advanced physics problems using artificial neural networks?
If you can translate a complex multi-dimentional non-linear physics problem into the domain of simple pattern matching and machine learning tools, then you might get some results from artificial neural networks...
Kinda like trying to service an F1 transmission using a glue gun, a roll of duck tape and a spork.
I remember one fast jerkoff (infamous for driving a toilet) being nasty to you for no apparent reason...
And why has nobody has mentioned MATH yet. When I started, he was the guy to beat. Even before Macest...
That's a naive analysis anyway. If the last race had been worth double points back in those past seasons, the teams and drivers may have tackled the final race differently. Some of the championships mentioned would have ended the same even if there had been double points for the last race, because the results of that final race would have been different.
E.g if you know you only need 5th in the final race, you might not take risks trying to battle for a higher finishing place... however, if the race is worth double points, maybe you need at least second, so will battle harder and take more chances.
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