CN113885835A - Random number generator and random number generation method - Google Patents

Random number generator and random number generation method Download PDF

Info

Publication number
CN113885835A
CN113885835A CN202111194005.9A CN202111194005A CN113885835A CN 113885835 A CN113885835 A CN 113885835A CN 202111194005 A CN202111194005 A CN 202111194005A CN 113885835 A CN113885835 A CN 113885835A
Authority
CN
China
Prior art keywords
current data
random number
random
bit sequence
interval
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111194005.9A
Other languages
Chinese (zh)
Inventor
赵原
张祺智
李漓春
殷山
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Alipay Hangzhou Information Technology Co Ltd
Original Assignee
Alipay Hangzhou Information Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Alipay Hangzhou Information Technology Co Ltd filed Critical Alipay Hangzhou Information Technology Co Ltd
Priority to CN202111194005.9A priority Critical patent/CN113885835A/en
Publication of CN113885835A publication Critical patent/CN113885835A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G06F7/58—Random or pseudo-random number generators
    • G06F7/588—Random number generators, i.e. based on natural stochastic processes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G06F7/58—Random or pseudo-random number generators
    • G06F7/582—Pseudo-random number generators
    • G06F7/586—Pseudo-random number generators using an integer algorithm, e.g. using linear congruential method

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)

Abstract

The embodiment of the specification provides a random number generator and a random number generation method. The random number generator includes: the parameter acquisition module is configured to acquire a first number of first random numbers allowed in a specified interval; a data sampling module configured to obtain a bit sequence from a stream of random numbers, 2nNot less than the first number, n characterizing the length of the bit sequence; a judging processing module configured to determine whether the current data represented by the bit sequence is an available random number, wherein when the current data is the available random number, the judging processing module is 2nRounding down the quotient of the first number, the product of the result of rounding down and the first number being not less than the current data, n characterizing the length of the bit sequence; and the operation processing module is configured to generate first random numbers which are uniformly distributed in the specified interval according to the lower limit value of the specified interval and the current data when the current data is the available random numbers.

Description

Random number generator and random number generation method
Technical Field
One or more embodiments of the present disclosure relate to the field of computers, and more particularly, to a random number generator and a random number generation method.
Background
A large number of random numbers are often used in technical scenarios such as secure Multi-Party computing (MPC) and Machine Learning (ML). A stream of random numbers consisting of 0 and 1 can be generated, typically using a physical random signal; then selecting a bit sequence with the length of n from the random number stream according to the actual service requirement, and taking the data represented by the bit sequence as a random number, wherein the random number is in the interval [0, 2 ]n-1]The inner part is uniformly distributed and the precision is 1.
A new technical solution is desired in order to be able to acquire random numbers that are evenly distributed within a specified interval.
Disclosure of Invention
One or more embodiments of the present specification provide a random number generator and a random number generation method.
In a first aspect, a random number generator is provided, comprising:
the parameter acquisition module is configured to acquire a first number of first random numbers allowed in a specified interval;
a data sampling module configured to obtain a bit sequence from a stream of random numbers, 2nNot less than the first number, n characterizing the length of the bit sequence;
a judgment processing module configured to determine whether current data represented by the bit sequence is an available random number, wherein when the current data is the available random number, the judgment processing module is 2nRounding down the quotient of the first number, wherein the product of the rounding down result and the first number is not less than the current data;
and the operation processing module is configured to generate first random numbers which are uniformly distributed in the specified interval according to the lower limit value of the specified interval and the current data when the current data is the available random numbers.
In a possible embodiment, the upper limit value and the lower limit value of the designated interval are both integers, and the first random numbers allowed in the designated interval are both integers.
In a possible wayIn the embodiment, 2nThe quotient to the first number is less than 2;
the judging and processing module is a discriminator and is used for judging whether the current data is smaller than the first quantity or not;
the operation processing module is an adder and is used for performing summation operation on the lower limit value of the designated interval and the current data to obtain a first random number under the condition that the current data is smaller than the first number.
In one possible embodiment, 2nThe quotient from the first number is not less than 2;
the judgment processing module comprises a second arithmetic unit and a discriminator; wherein the second arithmetic unit is used for pair 2nRounding down the quotient of the first number, the product of the result of rounding down and the first number being used as a reference value; the discriminator is used for judging whether the current data is smaller than the reference value or not, and if so, the operation processing module is triggered;
the operation processing module comprises a third operation unit and an adder; the third arithmetic unit is configured to, in response to the trigger of the discriminator, perform remainder taking on the current data by using the first number, and output an obtained remainder; the adder is configured to sum the remainder and a lower limit value of the specified interval to obtain a first random number.
In one possible embodiment, the stream of random numbers is generated based on sets of physically random signals.
In a second aspect, a random number generation method is provided, including:
determining a first number of allowed first random numbers in a designated interval;
obtaining a bit sequence from a stream of random numbers, 2nNot less than the first number, n characterizing the length of the bit sequence;
determining whether the current data characterized by the bit sequence is an available random number according to the first quantity, wherein when the current data is the available random number, the pair 2 isnQuotient to the first quantityRounding down, the product of the result of rounding down and the first number being not less than the current data, n characterizing the length of the bit sequence;
and when the current data is the available random number, generating first random numbers which are uniformly distributed in the specified interval according to the lower limit value of the specified interval and the current data.
In a possible embodiment, the upper limit value and the lower limit value of the designated interval are both integers, and the first random numbers allowed in the designated interval are both integers.
In one possible embodiment, 2nThe quotient to the first number is less than 2;
the determining whether the current data characterized by the bit sequence is an available random number according to the first quantity includes: when the current data is smaller than the first quantity, determining the current data to be an available random number;
the generating of the first random number uniformly distributed in the designated interval according to the lower limit value of the designated interval and the current data includes: and performing summation operation on the lower limit value of the designated interval and the current data to obtain a first random number.
In one possible embodiment, 2nThe quotient from the first number is not less than 2;
the determining whether the current data characterized by the bit sequence is an available random number according to the first quantity includes: to 2nRounding down the quotient of the first number, the product of the result of rounding down and the first number being used as a reference value; when the current data is smaller than the reference value, determining the current data to be an available random number;
the generating of the first random number uniformly distributed in the designated interval according to the lower limit value of the designated interval and the current data includes: and carrying out remainder operation on the current data by utilizing the first quantity, and carrying out summation operation on the obtained remainder and the lower limit value of the designated interval to obtain a first random number.
In one possible embodiment, the stream of random numbers is generated based on sets of physically random signals.
In a third aspect, there is provided a computer readable storage medium having stored thereon a computer program/instructions which, when executed in a computing device, causes the computing device to perform the method of any of the second aspects.
In a fourth aspect, there is provided a computing device comprising a memory having stored therein a computer program/instructions and a processor that, when executing the computer program/instructions, implements the method of any of the second aspects.
With the random number generator and the random number generation method provided in one or more embodiments of the present specification, a length n and 2 may be obtained from a random number stream according to a first number of first random numbers allowed in a specified intervalnNot less than the first number of bit sequences, when paired with 2nAnd rounding down the quotient of the first number, wherein when the product of the rounding-down result and the first number is not less than the current data represented by the bit sequence, the current data can be used as an available random number for generating the first random number, and then the first random numbers uniformly distributed in the specified interval can be generated according to the current data and the lower limit value of the specified interval.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of random number generation based on a physical random signal provided in an embodiment of the present description;
FIG. 2 is a schematic diagram of a random number generator provided in an embodiment of the present disclosure;
fig. 3 is a flowchart of a random number generation method provided in an embodiment of the present specification.
Detailed Description
Various non-limiting embodiments provided by the present specification are described in detail below with reference to the attached figures.
Referring to fig. 1, a physical random signal may be used to generate a stream of random numbers consisting of 0 and 1; for example, a plurality of arrays of light sources capable of emitting light continuously may be used to generate a plurality of independent sets of physical random signals, which may include, but are not limited to, light intensity signals and/or electromagnetic radiation signals; and then, carrying out fusion processing on the multiple groups of physical random signals, scrambling the physical signals subjected to the fusion processing, and finally outputting a random number stream consisting of 0 and 1. A random number stream consisting of 0 s and 1 s may also be generated by a pseudo random number generator/deterministic random bit generator based on a corresponding random seed. When random numbers need to be generated, a bit sequence with the length of n can be selected from the random number stream, and data represented by the bit sequence can be used as data in the interval [0, 2 ]n-1]Random number K1 with uniform distribution and precision of 1.
However, in some traffic scenarios, it may be desirable to obtain a signal in a specified interval [ a, b ]]Uniformly distributed random numbers, wherein the lower limit value a and the upper limit value b are signed integers, a may not be equal to 0, and b may not be equal to 2n-1, and the random number desired to be obtained is the interval [ a, b]The inner precision is a discrete value of 1. Random numbers are possible in technical scenes such as safe multiparty computation or machine learning, and in order to save computation amount or meet specific constraint conditions, generation in a specified interval of-3, 3 is desirable]Random numbers uniformly distributed therein; as another example, in the scenario of randomly reserving meeting dates, the allowed meeting dates may include 11-31 days of a month of a year, at which time it may be desirable to generate the meeting in the designated interval [11, 31 ]]Random numbers uniformly distributed therein; in another technical scenario in which a school randomly extracts students for thesis answering according to their school numbers by using a computer, it may be desirable to generate random numbers uniformly distributed in a designated interval corresponding to each school number.
The embodiments of the present disclosure provide at least a random number generator and a random number generation method, which can obtain random numbers that are uniformly distributed in a designated interval and have an accuracy of 1.
Fig. 2 is a schematic diagram of a random number generator provided in an embodiment of the present specification. The random number generator may be implemented in hardware, software, or a combination of hardware and software. It should be particularly noted that, when implemented by software, a computer program for implementing the random number generator may be specifically stored in a computer readable medium, and the computer program of the random number generator is allowed to be transmitted according to its corresponding instruction sequence, so that when the computer program for implementing the random number generator is executed by a computing device, the computing device implements the function of the random number generator provided in any one of the embodiments of the present specification, that is, the computing device executes the random number generation method provided in any one of the embodiments of the present specification. In addition, when implemented by hardware or a combination of hardware and software, some or all of the functional modules corresponding to the functions of the random number generator may be implemented as discrete devices, circuits including discrete devices, or components integrated by circuits and chips, which may independently implement specific functions by using software programs.
As shown in fig. 2, the random number generator may include at least: a parameter obtaining module 201 configured to obtain a first number of first random numbers allowed in a specified interval; a data sampling module 203 configured to obtain a bit sequence from a stream of random numbers, 2 of whichnNot less than the first number, n characterizing the length of the bit sequence; a judgment processing module 205 configured to determine whether the current data represented by the bit sequence is an available random number, wherein when the current data is the available random number, the judgment processing module is 2nRounding down the quotient with the first quantity, wherein the product of the rounding down result and the first quantity is not less than the current data; and the operation processing module 207 is configured to generate first random numbers which are uniformly distributed in the specified interval according to the lower limit value of the specified interval and the current data when the current data is the available random number.
For convenience of description, the specified interval continues to be represented by an interval [ a, b ], where an upper limit value b and a lower limit value a of the interval [ a, b ] are signed integers, and first random numbers allowed in the interval [ a, b ] are integers, that is, the first random numbers to be generated are integers which are uniformly distributed in the interval [ a, b ] and have an accuracy of 1. Accordingly, the first number of the first random numbers allowed in the interval [ a, b ] can be expressed as b-a + 1. The current data characterized by the bit sequence obtained from the stream of random numbers will be further denoted hereinafter by K1.
In one possible implementation, the data sampling module 203 includes a first arithmetic unit and a data sampling unit. Wherein the first arithmetic unit is configured to determine the length n, 2 of the bit sequence to be acquired according to b-a +1nNot less than b-a + 1. In particular, may be at 2nDetermining a value range satisfied by n by using b-a +1 or more as a constraint condition, and then extracting a single integer from the determined value range as the length n of the bit sequence to be acquired; the length n of the bit sequence to be acquired can also be obtained by rounding up the logarithm of base 2 b-a + 1. The data sampling unit is configured to obtain a bit sequence of length n from the stream of random numbers and to treat the data characterized by the bit sequence as current data K1.
Depending on the way the length n is obtained by the data sampling module 203, 2nThe quotient with b-a +1 can be divided into two cases, namely into 2nThe quotient of b-a +1 is less than 2 or not less than 2. For the two cases, the determination of whether the current data K1 is an available random number may be implemented by configuring different determination processing modules 205, and the generation of the first random number may be implemented by configuring different operation processing modules 207.
When 2 is innWhen the quotient of b-a +1 is less than 2, the judgment processing module 205 may be a discriminator for judging whether the current data K1 is less than b-a + 1; and if the current data K1 is less than b-a +1, the current data K1 is the usable random number capable of being used for generating the first random number, otherwise, the current data K1 is not the usable random number. The operation processing module 207 may be an adder for comparing the interval [ a, b ] when the current data K1 is less than b-a +1, i.e. when the current data K1 is an available random number]And a random number K1 to obtain a first randomAnd (4) counting.
It should be noted that, the current data K1 is in the interval [0, 2n-1]Inner uniform distribution, b-a is greater than 0 and less than or equal to 2n1, when the current data K1 is less than b-a +1, it means that the current data K1 must be in the interval [0, b-a ]]The inner parts are uniformly distributed; accordingly, please continue to refer to FIG. 1, with the intervals [ a, b]Essentially, the interval [0, b-a ] is divided on the number axis with the precision of 1]Shift a units to the right, for interval [ a, b]The first random number a + K1 obtained by summing the lower limit value a of (a) and the current data K1 is necessarily in the interval [ a, b ]]The inner parts are uniformly distributed.
When 2 is innWhen the quotient of b-a +1 is not less than 2, the judgment processing module 205 includes a second arithmetic unit and a discriminator; wherein the second arithmetic unit is used for pair 2nRounding down the quotient of b-a +1, and taking the product of the rounding down result and b-a +1 as a reference value MAX; the discriminator is used for judging whether the current data K1 is smaller than the reference value MAX, if so, the current data K1 is an available random number which can be used for generating the first random number, and the discriminator can trigger the operation processing module 207. Correspondingly, the arithmetic processing module 207 includes a third arithmetic unit and an adder; the third arithmetic unit is configured to carry out complementation on the current data K1 by using b-a +1 in response to the triggering of the discriminator and output an obtained remainder K2; the adder is configured to sum the remainder K2 and the interval [ a, b]Is summed to obtain a first random number.
It should be noted that, the current data K1 is in the interval [0, 2n-1]Inner uniform distribution, reference value MAX is greater than 0 and less than 2n-1, when the current data K1 is less than the reference value MAX means that the current data K1 is in the interval [0, MAX-1%]The inner parts are uniformly distributed. Correspondingly, since the quotient of the reference value MAX and b-a +1 is necessarily an integer, it means that the remainder K2 of the current data K1 and b-a +1 is necessarily [0, b-a +]Are uniformly distributed within the interval [ a, b]Is to divide the interval [0, b-a ] on the number axis with the precision of 1]Shift a units to the right, for interval [ a, b]The first random number a + K2 obtained by summing the lower limit value a and the remainder K2 is necessarily in the interval a, b]The inner parts are uniformly distributed.
Based on the same concept as the random number generator, the embodiment of the present specification further provides a random number generation method. As shown in fig. 3, the method may include at least the following steps 301-307.
In step 301, a first number of first random numbers allowed in a designated interval is determined.
Step 303, a bit sequence is obtained from the stream of random numbers. Wherein the length n, 2 of the bit sequence to be acquired can be first determined from the first numbernNot less than the first number; a bit sequence of length n is then obtained from the stream of random numbers, wherein the current data characterized by the bit sequence may be usable for generating available random numbers that are evenly distributed over a specified interval.
Step 305, it is determined whether the current data is an available random number. When the current data is an available random number that can be used to generate the first random number, pair 2nRounding down the quotient with the first quantity, the product of the result of rounding down and the first quantity being no less than the current data.
And 307, when the current data is the available random number, generating first random numbers which are uniformly distributed in the specified interval according to the lower limit value of the specified interval and the current data.
In one possible embodiment, when 2nWhen the quotient from the first number is less than 2, in step 305, it may be specifically determined that the current data is an available random number when the current data is less than the first number. In step 307, when the current data is an available random number, the lower limit value of the designated interval and the current data may be summed to obtain a first random number.
In another possible embodiment, when 2nWhen the quotient from the first number is not less than 2, step 305 may specifically be 2nRounding down the quotient of the first number, and taking the product of the rounding down result and the first number as a reference value; and determining the current data as an available random number when the current data is less than the reference value. In step 307, when the current data is an available random number, the first number may be used to perform remainder on the current data, and the obtained remainder and the lower limit value of the designated interval are summed to obtain the first random number.
Also provided in embodiments of this specification is a computer-readable storage medium having stored thereon a computer program/instructions which, when executed in a computing device, the computing device performs the random number generation method provided in any one of the embodiments of this specification.
The embodiment of the present specification further provides a computing device, which includes a memory and a processor, where the memory stores therein a computer program/instruction, and the processor, when executing the computer program/instruction, implements the random number generation method provided in any one embodiment of the present specification.
The embodiments in the present description are described in a progressive manner, and the same and similar parts in the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the method embodiment, since the function thereof is substantially similar to that of the random number generator provided in the apparatus embodiment, the description of the method embodiment is relatively simple, and in relation to the description of the apparatus embodiment, reference may be made to the description of the apparatus embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.

Claims (12)

1. A random number generator, comprising:
the parameter acquisition module is configured to acquire a first number of first random numbers allowed in a specified interval;
a data sampling module configured to obtain a bit sequence from a stream of random numbers, 2nNot less than the first number, n characterizing the length of the bit sequence;
a judgment processing module configured to determine whether current data represented by the bit sequence is an available random number, wherein when the current data is the available random number, the judgment processing module is 2nRounding down the quotient of the first number, wherein the product of the rounding down result and the first number is not less than the current data;
and the operation processing module is configured to generate first random numbers which are uniformly distributed in the specified interval according to the lower limit value of the specified interval and the current data when the current data is the available random numbers.
2. The random number generator of claim 1, wherein the upper and lower values of the specified interval are both integers, and the first random numbers allowed within the specified interval are both integers.
3. The random number generator of claim 1, wherein 2nThe quotient to the first number is less than 2;
the judging and processing module is a discriminator and is used for judging whether the current data is smaller than the first quantity or not;
the operation processing module is an adder and is used for performing summation operation on the lower limit value of the designated interval and the current data to obtain a first random number under the condition that the current data is smaller than the first number.
4. The random number generator of claim 1, wherein 2nThe quotient from the first number is not less than 2;
the judgment processing module comprises a second arithmetic unit anda discriminator; wherein the second arithmetic unit is used for pair 2nRounding down the quotient of the first number, the product of the result of rounding down and the first number being used as a reference value; the discriminator is used for judging whether the current data is smaller than the reference value or not, and if so, the operation processing module is triggered;
the operation processing module comprises a third operation unit and an adder; the third arithmetic unit is configured to, in response to the trigger of the discriminator, perform remainder taking on the current data by using the first number, and output an obtained remainder; the adder is configured to sum the remainder and a lower limit value of the specified interval to obtain a first random number.
5. The random number generator of any of claims 1-4, wherein the stream of random numbers is generated based on sets of physical random signals.
6. A random number generation method, comprising:
determining a first number of allowed first random numbers in a designated interval;
obtaining a bit sequence from a stream of random numbers, 2nNot less than the first number, n characterizing the length of the bit sequence;
determining whether the current data characterized by the bit sequence is an available random number according to the first quantity, wherein when the current data is the available random number, the pair 2 isnRounding down the quotient of the first number, wherein the product of the rounding down result and the first number is not less than the current data;
and when the current data is the available random number, generating first random numbers which are uniformly distributed in the specified interval according to the lower limit value of the specified interval and the current data.
7. The method according to claim 6, wherein the upper limit value and the lower limit value of the designated interval are both integers, and the first random numbers allowed in the designated interval are both integers.
8. The method of claim 6, wherein 2nThe quotient to the first number is less than 2;
the determining whether the current data characterized by the bit sequence is an available random number according to the first quantity includes: when the current data is smaller than the first quantity, determining the current data to be an available random number;
the generating of the first random number uniformly distributed in the designated interval according to the lower limit value of the designated interval and the current data includes: and performing summation operation on the lower limit value of the designated interval and the current data to obtain a first random number.
9. The method of claim 6, wherein 2nThe quotient from the first number is not less than 2;
the determining whether the current data characterized by the bit sequence is an available random number according to the first quantity includes: to 2nRounding down the quotient of the first number, the product of the result of rounding down and the first number being used as a reference value; when the current data is smaller than the reference value, determining the current data to be an available random number;
the generating of the first random number uniformly distributed in the designated interval according to the lower limit value of the designated interval and the current data includes: and carrying out remainder operation on the current data by utilizing the first quantity, and carrying out summation operation on the obtained remainder and the lower limit value of the designated interval to obtain a first random number.
10. The method of any of claims 6-9, wherein the stream of random numbers is generated based on sets of physically random signals.
11. A computer-readable storage medium having stored thereon a computer program which, when executed in a computing device, performs the method of any of claims 6-9.
12. A computing device comprising a memory having stored therein a computer program and a processor that, when executing the computer program, implements the method of any of claims 6-9.
CN202111194005.9A 2021-10-13 2021-10-13 Random number generator and random number generation method Pending CN113885835A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111194005.9A CN113885835A (en) 2021-10-13 2021-10-13 Random number generator and random number generation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111194005.9A CN113885835A (en) 2021-10-13 2021-10-13 Random number generator and random number generation method

Publications (1)

Publication Number Publication Date
CN113885835A true CN113885835A (en) 2022-01-04

Family

ID=79002748

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111194005.9A Pending CN113885835A (en) 2021-10-13 2021-10-13 Random number generator and random number generation method

Country Status (1)

Country Link
CN (1) CN113885835A (en)

Similar Documents

Publication Publication Date Title
Zuckerman General weak random sources
Lee et al. A hardware Gaussian noise generator using the Wallace method
KR102181385B1 (en) Generating randomness in neural networks
US20130191427A1 (en) Pseudo-noise generator
US5323338A (en) Pseudo-random sequence generators
Passerat-Palmbach et al. Reliable initialization of GPU-enabled parallel stochastic simulations using mersenne twister for graphics processors
CN113885835A (en) Random number generator and random number generation method
Brent et al. The great trinomial hunt
US20040054703A1 (en) Method and device for generating a pseudo-random sequence using a discrete logarithm
Wiegold Growth sequences of finite groups IV
Didolkar et al. Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving
Zembowicz et al. Automated discovery of empirical equations from data
Burton et al. Distributed random number generation
US11515995B2 (en) Efficient computation of univariate statistical moments for side channel vulnerability evaluation
Edmonds The generation of pseudo-random numbers on electronic digital computers
CN109582295B (en) Data processing method and device, storage medium and processor
CN112069508A (en) Method, system, device and medium for positioning vulnerability API (application program interface) parameters of machine learning framework
CN107122303B (en) Test method and device
Batelić et al. Improved circuits for a random pulse computer
Galias Dynamics of the Hénon map in the digital domain
US7072927B1 (en) Method and apparatus for generating random numbers for use in a field programmable gate array
Sakamoto et al. Efficient methods to generate constant sns with considering trade-off between error and overhead and its evaluation
CN113821442B (en) Pressure testing method, system, terminal and storage medium based on normal distribution
EP2372528A1 (en) Pseudo-noise generator
SU903873A1 (en) Generator of random numbers for simulating general population by objects of a sample

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination