If you bought Microsoft 365 Family and want to allow other users to install Office, please don't send the product key to them because they will see the same error. Instead, send them an email or link using the procedure in How to share your Office 365 Home subscription benefit. This will add their Microsoft accounts to your subscription and allow them to go to their Microsoft account, Services & subscriptions page to install Office apps on their own devices.
Bad Company 2 Error Serial Key
You can redeem up to 25 one-time purchases of Office 2019, 2016, or Office 2013 on the same Microsoft account. To redeem additional products, you'll need to create another Microsoft account. For more help, see Getting maximum number error when you try to redeem Office?
Okay bought Steam version of BFBC2 and have same problem as many of steam users apparently : unable to log in after reading agreement list and typing serial number error occurs about being unable to connect
I bought the second time the game in steam for the first time forgot the password and mail from account. But when I went into the game I saw that the serial key is the same for me. In support of the intelligible, nothing was said.
Now, when I launch the game and login with the same account I use to buy it in Origin, I'm asked to enter my serial number, which I do, but after hitting the submit button I get the "Unable to login" error, every, single, time.
Edit: Okay, so I found the "Redeem product code" in the Origin app, but now when I try to login to BFBC2 with the same account it still asks for the serial, but then says it's already in use, what the hell is going on here?
This article helps Intune administrators understand and troubleshoot error messages when enrolling Windows devices in Microsoft Intune. See Troubleshoot device enrollment in Microsoft Intune for additional, general troubleshooting scenarios.
You use both MDM for Microsoft 365 and Intune on the tenant. And the user who tries to enroll the device doesn't have a valid Intune license or an Office 365 license. In this situation, you may receive the following error message:
Error 80180026: "Something went wrong. Confirm you are using the correct sign-in information and that your organization uses this feature. You can try to do this again or contact your system administrator with the error code 80180026."
Error 0x80070774: Something went wrong. Confirm you are using the correct sign-in information and that your organization uses this feature. You can try to do this again or contact your system administrator with the error code 80070774.
Another possible cause for this error is that the Autopilot object's associated AzureAD device has been deleted. To resolve this issue, delete the Autopilot object and reimport the hash to generate a new one.
All things considered, when you get the 0xc00007b error, it generally means that some file in either the startup or activation process is corrupted. Not knowing exactly what your system configuration is, there is really no way to help you diagnose the problem. By this I mean the hardware configuration AND the software configuration.
Exactly where in the process did you receive this error? During the actual upgrade? During the reboot after the upgrade? When you tried to enter an activation key? After you entered an activation key? Where?
Floating-point arithmetic is considered an esoteric subject by many people. This is rather surprising because floating-point is ubiquitous in computer systems. Almost every language has a floating-point datatype; computers from PCs to supercomputers have floating-point accelerators; most compilers will be called upon to compile floating-point algorithms from time to time; and virtually every operating system must respond to floating-point exceptions such as overflow. This paper presents a tutorial on those aspects of floating-point that have a direct impact on designers of computer systems. It begins with background on floating-point representation and rounding error, continues with a discussion of the IEEE floating-point standard, and concludes with numerous examples of how computer builders can better support floating-point.
Categories and Subject Descriptors: (Primary) C.0 [Computer Systems Organization]: General -- instruction set design; D.3.4 [Programming Languages]: Processors -- compilers, optimization; G.1.0 [Numerical Analysis]: General -- computer arithmetic, error analysis, numerical algorithms (Secondary)
Additional Key Words and Phrases: Denormalized number, exception, floating-point, floating-point standard, gradual underflow, guard digit, NaN, overflow, relative error, rounding error, rounding mode, ulp, underflow.
Builders of computer systems often need information about floating-point arithmetic. There are, however, remarkably few sources of detailed information about it. One of the few books on the subject, Floating-Point Computation by Pat Sterbenz, is long out of print. This paper is a tutorial on those aspects of floating-point arithmetic (floating-point hereafter) that have a direct connection to systems building. It consists of three loosely connected parts. The first section, Rounding Error, discusses the implications of using different rounding strategies for the basic operations of addition, subtraction, multiplication and division. It also contains background information on the two methods of measuring rounding error, ulps and relative error. The second part discusses the IEEE floating-point standard, which is becoming rapidly accepted by commercial hardware manufacturers. Included in the IEEE standard is the rounding method for basic operations. The discussion of the standard draws on the material in the section Rounding Error. The third part discusses the connections between floating-point and the design of various aspects of computer systems. Topics include instruction set design, optimizing compilers and exception handling.
Squeezing infinitely many real numbers into a finite number of bits requires an approximate representation. Although there are infinitely many integers, in most programs the result of integer computations can be stored in 32 bits. In contrast, given any fixed number of bits, most calculations with real numbers will produce quantities that cannot be exactly represented using that many bits. Therefore the result of a floating-point calculation must often be rounded in order to fit back into its finite representation. This rounding error is the characteristic feature of floating-point computation. The section Relative Error and Ulps describes how it is measured.
Since most floating-point calculations have rounding error anyway, does it matter if the basic arithmetic operations introduce a little bit more rounding error than necessary? That question is a main theme throughout this section. The section Guard Digits discusses guard digits, a means of reducing the error when subtracting two nearby numbers. Guard digits were considered sufficiently important by IBM that in 1968 it added a guard digit to the double precision format in the System/360 architecture (single precision already had a guard digit), and retrofitted all existing machines in the field. Two examples are given to illustrate the utility of guard digits.
Since rounding error is inherent in floating-point computation, it is important to have a way to measure this error. Consider the floating-point format with = 10 and p = 3, which will be used throughout this section. If the result of a floating-point computation is 3.12 10-2, and the answer when computed to infinite precision is .0314, it is clear that this is in error by 2 units in the last place. Similarly, if the real number .0314159 is represented as 3.14 10-2, then it is in error by .159 units in the last place. In general, if the floating-point number d.d...d e is used to represent z, then it is in error by d.d...d - (z/e)p-1 units in the last place.4, 5 The term ulps will be used as shorthand for "units in the last place." If the result of a calculation is the floating-point number nearest to the correct result, it still might be in error by as much as .5 ulp. Another way to measure the difference between a floating-point number and the real number it is approximating is relative error, which is simply the difference between the two numbers divided by the real number. For example the relative error committed when approximating 3.14159 by 3.14 100 is .00159/3.14159 .0005.
To compute the relative error that corresponds to .5 ulp, observe that when a real number is approximated by the closest possible floating-point number d.dd...dd e, the error can be as large as 0.00...00' e, where ' is the digit /2, there are p units in the significand of the floating-point number, and p units of 0 in the significand of the error. This error is ((/2)-p) e. Since numbers of the form d.dd...dd e all have the same absolute error, but have values that range between e and e, the relative error ranges between ((/2)-p) e/e and ((/2)-p) e/e+1. That is,
In particular, the relative error corresponding to .5 ulp can vary by a factor of . This factor is called the wobble. Setting = (/2)-p to the largest of the bounds in (2) above, we can say that when a real number is rounded to the closest floating-point number, the relative error is always bounded by e, which is referred to as machine epsilon.
In the example above, the relative error was .00159/3.14159 .0005. In order to avoid such small numbers, the relative error is normally written as a factor times , which in this case is = (/2)-p = 5(10)-3 = .005. Thus the relative error would be expressed as (.00159/3.14159)/.005) 0.1.
To illustrate the difference between ulps and relative error, consider the real number x = 12.35. It is approximated by = 1.24 101. The error is 0.5 ulps, the relative error is 0.8. Next consider the computation 8. The exact value is 8x = 98.8, while the computed value is 8 = 9.92 101. The error is now 4.0 ulps, but the relative error is still 0.8. The error measured in ulps is 8 times larger, even though the relative error is the same. In general, when the base is , a fixed relative error expressed in ulps can wobble by a factor of up to . And conversely, as equation (2) above shows, a fixed error of .5 ulps results in a relative error that can wobble by . 2ff7e9595c
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