An efficient quantum meet-in-the-middle attack against NTRU-2005

Size: px
Start display at page:

Download "An efficient quantum meet-in-the-middle attack against NTRU-2005"

Transcription

1 Article Quantum Information October 03 Vol58 o8-9: oi: 0007/s y An efficient quantum meet-in-the-mile attack against TRU-005 WAG Hong, MA Zhi * & MA huangui State Key Laboratory of Mathematical Engineering an Avance omputing, Zhengzhou 45000, hina Receive January, 03 accepte May 5, 03 TRU is one of the most iely use public-key cryptosystems an its security has been an active research topic This paper proposes a ne ay to fin TRU-005 private key The algorithm is base on meet-in-the-mile attack an a quantum algorithm for searching the fixe eight target ompare ith the current classical an quantum meet-in-the-mile attacks, our algorithm has loer time an space complexity Moreover, this attack can also be applie against ifferent versions of TRU The result can help to unerstan the security of TRU better quantum algorithm, TRU, meet-in-the-mile attack itation: Wang H, Ma Z, Ma G An efficient quantum meet-in-the-mile attack against TRU-005 hin Sci Bull, 03, 58: , oi: 0007/ s y For all the time, ho to use the quantum computational theory to improve the classical cryptanalysis ability is an important issue TRU is a public-key cryptosystem base on the shortest lattice vector problem At equivalent security level, TRU nees loer memory an smaller computational complexity than RSA o, there is no efficient quantum algorithm knon that ill solve the shortest lattice vector problem So, it is believe that TRU is secure in quantum times [] In fact, ith the rapi evelopment of quantum computation, all cryptosystems base on the problems of large integer factorization an iscrete logarithm are potentially fragile Hoever, it is still unclear hat kin of effects the quantum computational theory coul make on the security of TRU till no lassical meet-in-the-mile (MITM) attack is a generic cryptanalytic metho originally evelope from cryptanalysis of block ciphers Recently, this technique is also foun to be quite useful in the cryptanalysis of public-key cryptography MITM attack is the best algorithm for attacking TRU at present Grover [] propose a generic quantum search algorithm hich gives a quaratic speeup over the classical brute-force search Hoever, it is not yet knon *orresponing author ( ma_zhi@63com) hether Grover algorithm can spee up the classical MITM attack There are some ne evelopments in the classical cryptanalysis of TRU, such as lattice attack, hybri attack [3], broacast attack [4], etc Luig [5] combine lattice reuction technique ith Grover algorithm, an put forar a novel quantum attack algorithm against TRU Hoever, the attack algorithm in [5] is not better than classical MITM attack In 0, a quantum algorithm use to fin fixe eight target as propose [6] At the same time, the author analyze the security of TRU by the propose algorithm The computation complexity of Wang s algorithm is significantly loer than a classical brute-force search, but still higher than a classical MITM attack Xiong et al [7] combine MITM attack ith Grover quantum searching algorithm, an evelope a quantum MITM attack metho against TRU The time complexity O, hich is loer than the classical in [7] is MITM attack Hoever, the author just consiere the quantum iterative times, an ignore all the complexity of classical precomputation If consiering the classical complexity, the complexity of MITM attack in [7] The Author(s) 03 This article is publishe ith open access at Springerlinkcom csbscichinacom springercom/scp

2 Wang H, et al hin Sci Bull October (03) Vol58 o is log, hich has no any avantages compare ith the classical MITM attack in [8] Base on the classical MITM attack an a quantum algorithm to fin a target solution ith fixe eight, e propose a ne quantum algorithm to attack TRU The time com- plexity is only /3 log /3 /3 /3 (Here, x enotes the largest integer less than x ), hich is loer than previous attacks Preliminaries TRU cryptosystem TRU has been accepte as one of the stanar public-key cryptosystem in the stanar IEEE St 363 The avantage of TRU over other cryptosystems is that encryption an ecryption are very fast an the key are relatively small Also the key generation is fast an easy There have been several ifferent versions of TRU [9,0] For the completeness, e give a simple escription of TRU key generation Details can be foun in [0] To keep consistent ith [6,7], e use the same notation (The versions in [6] is TRU-005) In TRU, given three integers, p, q>0, an the basic objects are truncate polynomials in the ring R [ x]/ x Every element in the ring R can be represente as a vector or a polynomial, here multiplication is efine as convolution multiplication There are three nonempty proper subsets: Sr, Sg, SF R, here element rsr S( ) means that there are coefficients of one in polynomial r, an the rest are zero Similarly, one can efine S g =S( ), S F =S( 3 ) Step Ranomly choose g S an F SF, calculate f=+pf Here e require that there exists that f fq (Mo q) Step Let h fq g(mo q) The public key of TRU: h, p, q The private key of TRU: f A classical MITM attack hoose an integer k, such that k is much larger than g fq q R such In [8], the private key f=f, an the public key h=f q g (Mo q) Let f=f f, here both f an f have a length of /, containing / ones, an enote concatenation () Enumerate f Put each f into a bin base on the most significant bits of the first k coorinates of f h(mo q) () Enumerate f Juge each f to see if it correspons to an occupie bin While checking for occupation, e consier not only the bin given by the most significant bits of the first k coefficients of f h(mo q), but also the bins given by the flips of all the most significant bits hich a to the corresponing coefficients of f h(mo q) (3) Search for matches When f hits an occupie bin, take f from the bin Determine hether (f f ) h(mo q) is binary, an if so return f=f f an terminate Otherise, procee to the next f heck each one if the bin contains more than one f The algorithm can alays return the private key f or a cyclic shift of f Both the time an space complexity are ( ) 3 Wang s attack algorithm Wang et al [6] put forar a quantum algorithm to fin the fixe eight target, an propose a ne metho to fin the private key of TRU Wang s algorithm can be consiere as a quantum brute-force attack, hich can reuce the time O complexity from to First, Wang et al efine the label of an n-imensional Boolean vector ith fixe eight Definition [6] Suppose that the eight of an n-imensional Boolean vector v is, that is, in all positions v takes value 0 except at positions a, a,,a ith a <a <a n The label L v of vector v is then efine as L v a a a Obviously,there is a one-to-one corresponence beteen vectors an their labels Moreover, Lv n The label of a target vector is calle the target label Wang s attack proceure is as follos: () ompute the maximum value of labels n, enote t log n () Search the t-tuple vector using Grover algorithm, erive the target label (3) Derive target vector from target label, return as a caniate private key 4 A quantum MITM Attack In [7], the author assume an are even The bin hich contains polynomial f i ill be labele as label _ f i, an bin( fi) {label _ fi} The basic iea of quantum MITM attack in [7] is: () ompute all {label _ f, f } an arrange as a table L inexe by label _ f () Search f ith the Grover search algorithm, ith label _ f bin( f ), an

3 356 Wang H, et al hin Sci Bull October (03) Vol58 o8-9 f f h(mo q) {0,} (3) Search f correspon to label _ f in L Verify f +f ith other conitions In fact, the time complexity analysis in [7] is incorrect Just as e mentione before, the author ignore all the complexity of classical precomputation An improve quantum MITM attack In TRU-005, the private key is f=+f, here polynomial (or vector) F is consisting of ones an zeros So, if e can fin the polynomial F, the private key f can also been obtaine easily Here, the polynomial F can also be regare as a vector The basic iea of the improve algorithm The iea is to fin the key F in the form F F, here enotes concatenation The length of F an F are 3 an 3, respectively Moreover, F has 3 ones, an F has 3 ones We have fh g(mo q) F F h g(mo q) hf h gf h(mo q) h F h {0,} F h (Mo q) i i i i In fact, accoring to lemma, although F itself may not have the property, e kno that there exist some rotations of F hich has this property an that any rotation of F ill be effective as the private key parameters Lemma Let F F F, 3 an 3 are the length of F an F, respectively Then there exists one rotation of F hich has the property: F has 3 ones, an F has 3 ones Proof Let a, b>0 Without loss of generality, let F has 3 a ones in the first 3 entries, b ones in the mile 3 a b ones in the entries, an /3 last 3 entries Then, rotating F by one position can only change the number of ones in the first (mile) 3 entries by 0, or There are three cases: In case, if b 3, then after 3 left rotations each at a position, obviously In case, if b 3, hen finishing 3 left rotations, the first 3 entries ill have b 3 ones in them Therefore, at some points, the number of ones in the first 3 entries must have been exactly 3 o let us look at the last case If b 3, then 3a b 3, so after 3 left rotations, the first 3 entries ill have 3a b 3 ones in them, obviously Let T an S enote the binary vectors hich are efine by the most significant bits of the first k coorinates of h F h(mo q) an F h(mo q), respectively Here, all F are of length 3, but e ientify them ith the length- vectors forme by appening 3 zeros Similarly, e ientify all F ith the length- vectors forme by prepening 3 zeros Algorithm (i) hoose an integer k, so that k /3 /3 00* (ii) alculate each F to get the binary vector T, an arrange as a table L inexe by T (iii) Apply the Oracle, let F run over its hole sample space an then, search for matches by quantum algorithm in [6] The proper matches meet the to conitions: () S {} T or S {} T, here S is given by the flips of some bits of S hich a to the corresponing coefficients of F h(mo q) h F F h(mo q) {0,} () (iv) Verify f F F ith other conitions Details of searching for matches in step (iii) are: () Let the label l correspon to the vector F,l The Oracle can be efine as, if F, l meet the to conitions in step (iii) Ol () 0, others let () alculate the maximum value of the label n log /3 /3 /3 /3 (3) Initialize the quantum system, an prouce the n equally-eighte superposition state 0 n l l 0 n/ (4) Use Grover algorithm 4 times, get the label l an the vector F,l correspon to the label Algorithm analysis Let O() enote the complexity of classical computation Similarly, O() enotes the complexity of quantum computation Furthermore, to keep consistent ith [,8], the time to calculate h F h(mo q) is taken to be one,

4 Wang H, et al hin Sci Bull October (03) Vol58 o Table Time an space complexity comparison for ifferent TRU attacks Time Space -MITM [8] Wang s attack [6] Q-MITM [7] This paper O O / O / / / / / log / / O / O () / O log /3 /3 /3 /3 /3 /3 /3 /3 Table Time complexity comparison for various parameters Time complexity -MITM [8] Wang s attack [6] Q-MITM [7] This paper O / O log / / / / O / / O log /3 /3 /3 /3 /3 /3 TRU TRU TRU TRU operation Accoring to the above process, the time an space complexity of algorithm epen on step (ii) an step (iii) The number of F is /3 /3, so the time to put each F /3 into a proper bin is /3 alculating the table L nees /3 log /3 /3 /3 basic operation So the expecte time to run step (ii) ill be no more than /3 log /3 /3 /3 The computation complexity of step (iii) only epens on /3 Grover iterative times, ie O /3 The table L nees to be save, so the space complexity is /3 /3 3 omparison for ifferent TRU attacks The comparisons of results for ifferent attacks an various parameters are illustrate in Table an Table Remark ompare the to tables above, our metho is very efficient both in the time an space complexity In fact, if quantum computation is not more expensive than classical computation, it oul be orthhile to transfer some ones from the F sie to the F sie In this case, the expecte running time become approximately O /3 /3 /3 log /3 /3 /3 4 onclusions With the evelopment of quantum computation, the security strength of TRU has been an active research area in the past 0 years This paper revise some errors in [7] an propose an improve quantum MITM attack against TRU The time complexity in this paper is significantly reuce Moreover, this attack can also be applie against TRU- 998 an TRU-00 The result can help to unerstan the security of TRU better An open question orth investing oul be to see if the current attacks may still be improve This ork as supporte by the ational High Technology Research an Development Program of hina (0AA00803), the ational atural Science Founation of hina (U0460) an the Open Project Program of the State Key Laboratory of Mathematical Engineering an Avance omputing (03A4) Perlner R A, ooper D A Quantum resistant public key cryptography: A survey In: Seamons K, McBurnett, Polk T, es Proceeings of the 8th Symposium on Ientity an Trust on the Internet, 009 April 4 6, Gaithersburg, MD, USA e York: AM Press, Grover L K A fast quantum mechanics algorithm for atabase search In: Proceeing of the 8th AM Symposium on Theory of omputation, Philaelphia, PA, USA e York: AM Press, Hograve-Graham A hybri lattice-reuction an meet-in-themile attack against TRU In: Menezes A, e Proceeings RYPTO, 007 August 9 3, Santa Barbara, A, USA Berlin Heielberg: Springer, LS 46, Ding J T, Pan Y B, Deng Y P An algebraic broacast attack against TRU In: Susilo W, Mu Y, Seberry J, es Proceeings AISP, 0 July 9, Wollongong, SW, Australia Berlin Heielberg: Springer, LS 737,

5 358 Wang H, et al hin Sci Bull October (03) Vol58 o8-9 5 Luig A faster lattice reuction metho using quantum search In: Ibaraki T, Katoh, Ono H, es Proceeings ISAA, 003 December 5 7, Kyoto, Japan Berlin Heielberg: Springer, LS 906, Wang X, Bao W S, Fu X Q A quantum algorithm for searching a target solution of fixe eight hin Sci Bull, 0, 56: Xiong Z, Wang J, Wang Y, et al An improve MITM attack against TRU Int J Sec App, 0, 6: Silverman J, Olyzko A TRU Report 004, Version, A Meet-The Mile Attack on an TRU Private Key Technical Report, TRU ryptosystems, Hoffstein J, Pipher J, Silverman J TRU: A ring-base public key cryptosystem In: Buhler J P, e Proceeings Algorithmic umber Theory (ATS III), 998 June 5, Portlan, Oregon, USA Berlin Heielberg: Springer, LS 43, Hograve-Graham, Silverman J H, Whyte W hoosing parameter sets for TRUEncrypt ith AEP an SVES-3 In: Menezes A, e Proceeings the ryptographers Track at the RSA, 005 February 4 8, San Francisco, A, USA Berlin Heielberg: Springer, LS 3376, Open Access This article is istribute uner the terms of the reative ommons Attribution License hich permits any use, istribution, an reprouction in any meium, provie the original author(s) an source are creite

LATTICE-BASED D-OPTIMUM DESIGN FOR FOURIER REGRESSION

LATTICE-BASED D-OPTIMUM DESIGN FOR FOURIER REGRESSION The Annals of Statistics 1997, Vol. 25, No. 6, 2313 2327 LATTICE-BASED D-OPTIMUM DESIGN FOR FOURIER REGRESSION By Eva Riccomagno, 1 Rainer Schwabe 2 an Henry P. Wynn 1 University of Warwick, Technische

More information

A New Vulnerable Class of Exponents in RSA

A New Vulnerable Class of Exponents in RSA A ew Vulnerable Class of Exponents in RSA Aberrahmane itaj Laboratoire e Mathématiues icolas Oresme Campus II, Boulevar u Maréchal Juin BP 586, 4032 Caen Ceex, France. nitaj@math.unicaen.fr http://www.math.unicaen.fr/~nitaj

More information

A Note on Exact Solutions to Linear Differential Equations by the Matrix Exponential

A Note on Exact Solutions to Linear Differential Equations by the Matrix Exponential Avances in Applie Mathematics an Mechanics Av. Appl. Math. Mech. Vol. 1 No. 4 pp. 573-580 DOI: 10.4208/aamm.09-m0946 August 2009 A Note on Exact Solutions to Linear Differential Equations by the Matrix

More information

Can Punctured Rate-1/2 Turbo Codes Achieve a Lower Error Floor than their Rate-1/3 Parent Codes?

Can Punctured Rate-1/2 Turbo Codes Achieve a Lower Error Floor than their Rate-1/3 Parent Codes? Can Puncture Rate-1/2 Turbo Coes Achieve a Loer Error Floor than their Rate-1/3 Parent Coes? Ioannis Chatzigeorgiou, Miguel R. D. Rorigues, Ian J. Wassell Digital Technology Group, Computer Laboratory

More information

Lie symmetry and Mei conservation law of continuum system

Lie symmetry and Mei conservation law of continuum system Chin. Phys. B Vol. 20, No. 2 20 020 Lie symmetry an Mei conservation law of continuum system Shi Shen-Yang an Fu Jing-Li Department of Physics, Zhejiang Sci-Tech University, Hangzhou 3008, China Receive

More information

Agmon Kolmogorov Inequalities on l 2 (Z d )

Agmon Kolmogorov Inequalities on l 2 (Z d ) Journal of Mathematics Research; Vol. 6, No. ; 04 ISSN 96-9795 E-ISSN 96-9809 Publishe by Canaian Center of Science an Eucation Agmon Kolmogorov Inequalities on l (Z ) Arman Sahovic Mathematics Department,

More information

State-Space Model for a Multi-Machine System

State-Space Model for a Multi-Machine System State-Space Moel for a Multi-Machine System These notes parallel section.4 in the text. We are ealing with classically moele machines (IEEE Type.), constant impeance loas, an a network reuce to its internal

More information

Table of Common Derivatives By David Abraham

Table of Common Derivatives By David Abraham Prouct an Quotient Rules: Table of Common Derivatives By Davi Abraham [ f ( g( ] = [ f ( ] g( + f ( [ g( ] f ( = g( [ f ( ] g( g( f ( [ g( ] Trigonometric Functions: sin( = cos( cos( = sin( tan( = sec

More information

ELEC3114 Control Systems 1

ELEC3114 Control Systems 1 ELEC34 Control Systems Linear Systems - Moelling - Some Issues Session 2, 2007 Introuction Linear systems may be represente in a number of ifferent ways. Figure shows the relationship between various representations.

More information

Least-Squares Regression on Sparse Spaces

Least-Squares Regression on Sparse Spaces Least-Squares Regression on Sparse Spaces Yuri Grinberg, Mahi Milani Far, Joelle Pineau School of Computer Science McGill University Montreal, Canaa {ygrinb,mmilan1,jpineau}@cs.mcgill.ca 1 Introuction

More information

Computing Exact Confidence Coefficients of Simultaneous Confidence Intervals for Multinomial Proportions and their Functions

Computing Exact Confidence Coefficients of Simultaneous Confidence Intervals for Multinomial Proportions and their Functions Working Paper 2013:5 Department of Statistics Computing Exact Confience Coefficients of Simultaneous Confience Intervals for Multinomial Proportions an their Functions Shaobo Jin Working Paper 2013:5

More information

Lower bounds on Locality Sensitive Hashing

Lower bounds on Locality Sensitive Hashing Lower bouns on Locality Sensitive Hashing Rajeev Motwani Assaf Naor Rina Panigrahy Abstract Given a metric space (X, X ), c 1, r > 0, an p, q [0, 1], a istribution over mappings H : X N is calle a (r,

More information

JUST THE MATHS UNIT NUMBER DIFFERENTIATION 2 (Rates of change) A.J.Hobson

JUST THE MATHS UNIT NUMBER DIFFERENTIATION 2 (Rates of change) A.J.Hobson JUST THE MATHS UNIT NUMBER 10.2 DIFFERENTIATION 2 (Rates of change) by A.J.Hobson 10.2.1 Introuction 10.2.2 Average rates of change 10.2.3 Instantaneous rates of change 10.2.4 Derivatives 10.2.5 Exercises

More information

On the Enumeration of Double-Base Chains with Applications to Elliptic Curve Cryptography

On the Enumeration of Double-Base Chains with Applications to Elliptic Curve Cryptography On the Enumeration of Double-Base Chains with Applications to Elliptic Curve Cryptography Christophe Doche Department of Computing Macquarie University, Australia christophe.oche@mq.eu.au. Abstract. The

More information

Linear First-Order Equations

Linear First-Order Equations 5 Linear First-Orer Equations Linear first-orer ifferential equations make up another important class of ifferential equations that commonly arise in applications an are relatively easy to solve (in theory)

More information

Practical Analysis of Key Recovery Attack against Search-LWE Problem

Practical Analysis of Key Recovery Attack against Search-LWE Problem Practical Analysis of Key Recovery Attack against Search-LWE Problem IMI Cryptography Seminar 28 th June, 2016 Speaker* : Momonari Kuo Grauate School of Mathematics, Kyushu University * This work is a

More information

Situation awareness of power system based on static voltage security region

Situation awareness of power system based on static voltage security region The 6th International Conference on Renewable Power Generation (RPG) 19 20 October 2017 Situation awareness of power system base on static voltage security region Fei Xiao, Zi-Qing Jiang, Qian Ai, Ran

More information

A Weak First Digit Law for a Class of Sequences

A Weak First Digit Law for a Class of Sequences International Mathematical Forum, Vol. 11, 2016, no. 15, 67-702 HIKARI Lt, www.m-hikari.com http://x.oi.org/10.1288/imf.2016.6562 A Weak First Digit Law for a Class of Sequences M. A. Nyblom School of

More information

Extension of de Weger s Attack on RSA with Large Public Keys

Extension of de Weger s Attack on RSA with Large Public Keys Extension of e Weger s Attack on RSA with Large Public Keys Nicolas T. Courtois, Theoosis Mourouzis an Pho V. Le Department of Computer Science, University College Lonon, Gower Street, Lonon, U.K. {n.courtois,

More information

Efficient RNS bases for Cryptography

Efficient RNS bases for Cryptography 1 Efficient RNS bases for Cryptography Jean-Claue Bajar, Nicolas Meloni an Thomas Plantar LIRMM UMR 5506, University of Montpellier, France, {bajar,meloni,plantar}@lirmm.fr Abstract Resiue Number Systems

More information

COUNTING VALUE SETS: ALGORITHM AND COMPLEXITY

COUNTING VALUE SETS: ALGORITHM AND COMPLEXITY COUNTING VALUE SETS: ALGORITHM AND COMPLEXITY QI CHENG, JOSHUA E. HILL, AND DAQING WAN Abstract. Let p be a prime. Given a polynomial in F p m[x] of egree over the finite fiel F p m, one can view it as

More information

d dx But have you ever seen a derivation of these results? We ll prove the first result below. cos h 1

d dx But have you ever seen a derivation of these results? We ll prove the first result below. cos h 1 Lecture 5 Some ifferentiation rules Trigonometric functions (Relevant section from Stewart, Seventh Eition: Section 3.3) You all know that sin = cos cos = sin. () But have you ever seen a erivation of

More information

Euler equations for multiple integrals

Euler equations for multiple integrals Euler equations for multiple integrals January 22, 2013 Contents 1 Reminer of multivariable calculus 2 1.1 Vector ifferentiation......................... 2 1.2 Matrix ifferentiation........................

More information

Level Construction of Decision Trees in a Partition-based Framework for Classification

Level Construction of Decision Trees in a Partition-based Framework for Classification Level Construction of Decision Trees in a Partition-base Framework for Classification Y.Y. Yao, Y. Zhao an J.T. Yao Department of Computer Science, University of Regina Regina, Saskatchewan, Canaa S4S

More information

ALGEBRAIC AND ANALYTIC PROPERTIES OF ARITHMETIC FUNCTIONS

ALGEBRAIC AND ANALYTIC PROPERTIES OF ARITHMETIC FUNCTIONS ALGEBRAIC AND ANALYTIC PROPERTIES OF ARITHMETIC FUNCTIONS MARK SCHACHNER Abstract. When consiere as an algebraic space, the set of arithmetic functions equippe with the operations of pointwise aition an

More information

Quantum-resistant cryptography

Quantum-resistant cryptography Quantum-resistant cryptography Background: In quantum computers, states are represented as vectors in a Hilbert space. Quantum gates act on the space and allow us to manipulate quantum states with combination

More information

u!i = a T u = 0. Then S satisfies

u!i = a T u = 0. Then S satisfies Deterministic Conitions for Subspace Ientifiability from Incomplete Sampling Daniel L Pimentel-Alarcón, Nigel Boston, Robert D Nowak University of Wisconsin-Maison Abstract Consier an r-imensional subspace

More information

Implementing Gentry s Fully-Homomorphic Encryption Scheme Preliminary Report

Implementing Gentry s Fully-Homomorphic Encryption Scheme Preliminary Report Implementing Gentry s Fully-Homomorphic Encryption Scheme Preliminary Report Craig Gentry Shai Halevi August 5, 2010 Abstract We escribe a working implementation of a variant of Gentry s fully homomorphic

More information

Lecture 6 : Dimensionality Reduction

Lecture 6 : Dimensionality Reduction CPS290: Algorithmic Founations of Data Science February 3, 207 Lecture 6 : Dimensionality Reuction Lecturer: Kamesh Munagala Scribe: Kamesh Munagala In this lecture, we will consier the roblem of maing

More information

Lecture Introduction. 2 Examples of Measure Concentration. 3 The Johnson-Lindenstrauss Lemma. CS-621 Theory Gems November 28, 2012

Lecture Introduction. 2 Examples of Measure Concentration. 3 The Johnson-Lindenstrauss Lemma. CS-621 Theory Gems November 28, 2012 CS-6 Theory Gems November 8, 0 Lecture Lecturer: Alesaner Mąry Scribes: Alhussein Fawzi, Dorina Thanou Introuction Toay, we will briefly iscuss an important technique in probability theory measure concentration

More information

Solving DLP with Auxiliary Input over an Elliptic Curve Used in TinyTate Library

Solving DLP with Auxiliary Input over an Elliptic Curve Used in TinyTate Library Solving DLP with Auxiliary Input over an Elliptic Curve Use in TinyTate Library Yumi Sakemi 1,TetsuyaIzu 2, Masahiko Takenaka 2, an Masaya Yasua 2 1 Okayama University 3-1-1, Tsushima-naka, Kita-ku, Okayama,

More information

Practical Analysis of Key Recovery Attack against Search-LWE Problem

Practical Analysis of Key Recovery Attack against Search-LWE Problem Practical Analysis of Key Recovery Attack against Search-LWE Problem Royal Holloway an Kyushu University Workshop on Lattice-base cryptography 7 th September, 2016 Momonari Kuo Grauate School of Mathematics,

More information

Efficient Construction of Semilinear Representations of Languages Accepted by Unary NFA

Efficient Construction of Semilinear Representations of Languages Accepted by Unary NFA Efficient Construction of Semilinear Representations of Languages Accepte by Unary NFA Zeněk Sawa Center for Applie Cybernetics, Department of Computer Science Technical University of Ostrava 17. listopau

More information

FLUCTUATIONS IN THE NUMBER OF POINTS ON SMOOTH PLANE CURVES OVER FINITE FIELDS. 1. Introduction

FLUCTUATIONS IN THE NUMBER OF POINTS ON SMOOTH PLANE CURVES OVER FINITE FIELDS. 1. Introduction FLUCTUATIONS IN THE NUMBER OF POINTS ON SMOOTH PLANE CURVES OVER FINITE FIELDS ALINA BUCUR, CHANTAL DAVID, BROOKE FEIGON, MATILDE LALÍN 1 Introuction In this note, we stuy the fluctuations in the number

More information

Attacking Unbalanced RSA-CRT Using SPA

Attacking Unbalanced RSA-CRT Using SPA Attacking Unbalance RSA-CRT Using SPA Pierre-Alain Fouque, Gwenaëlle Martinet, an Guillaume Poupar DCSSI Crypto Lab 51, Boulevar e Latour-Maubourg 75700 Paris 07 SP, France Pierre-Alain.Fouque@ens.fr Gwenaelle.Martinet@worlonline.fr

More information

SYNCHRONOUS SEQUENTIAL CIRCUITS

SYNCHRONOUS SEQUENTIAL CIRCUITS CHAPTER SYNCHRONOUS SEUENTIAL CIRCUITS Registers an counters, two very common synchronous sequential circuits, are introuce in this chapter. Register is a igital circuit for storing information. Contents

More information

Math 1B, lecture 8: Integration by parts

Math 1B, lecture 8: Integration by parts Math B, lecture 8: Integration by parts Nathan Pflueger 23 September 2 Introuction Integration by parts, similarly to integration by substitution, reverses a well-known technique of ifferentiation an explores

More information

WEIGHTING A RESAMPLED PARTICLE IN SEQUENTIAL MONTE CARLO. L. Martino, V. Elvira, F. Louzada

WEIGHTING A RESAMPLED PARTICLE IN SEQUENTIAL MONTE CARLO. L. Martino, V. Elvira, F. Louzada WEIGHTIG A RESAMPLED PARTICLE I SEQUETIAL MOTE CARLO L. Martino, V. Elvira, F. Louzaa Dep. of Signal Theory an Communic., Universia Carlos III e Mari, Leganés (Spain). Institute of Mathematical Sciences

More information

Hyperbolic Moment Equations Using Quadrature-Based Projection Methods

Hyperbolic Moment Equations Using Quadrature-Based Projection Methods Hyperbolic Moment Equations Using Quarature-Base Projection Methos J. Koellermeier an M. Torrilhon Department of Mathematics, RWTH Aachen University, Aachen, Germany Abstract. Kinetic equations like the

More information

5.4 Fundamental Theorem of Calculus Calculus. Do you remember the Fundamental Theorem of Algebra? Just thought I'd ask

5.4 Fundamental Theorem of Calculus Calculus. Do you remember the Fundamental Theorem of Algebra? Just thought I'd ask 5.4 FUNDAMENTAL THEOREM OF CALCULUS Do you remember the Funamental Theorem of Algebra? Just thought I' ask The Funamental Theorem of Calculus has two parts. These two parts tie together the concept of

More information

Thermal conductivity of graded composites: Numerical simulations and an effective medium approximation

Thermal conductivity of graded composites: Numerical simulations and an effective medium approximation JOURNAL OF MATERIALS SCIENCE 34 (999)5497 5503 Thermal conuctivity of grae composites: Numerical simulations an an effective meium approximation P. M. HUI Department of Physics, The Chinese University

More information

Permanent vs. Determinant

Permanent vs. Determinant Permanent vs. Determinant Frank Ban Introuction A major problem in theoretical computer science is the Permanent vs. Determinant problem. It asks: given an n by n matrix of ineterminates A = (a i,j ) an

More information

Implementation of Automatic Invertible Matrix Mechanism in NTRU Matrix Formulation Algorithm

Implementation of Automatic Invertible Matrix Mechanism in NTRU Matrix Formulation Algorithm Implementation of Automatic Invertible Matrix Mechanism in NTRU Matrix Formulation Algorithm Mohan Rao Mamdikar, Vinay Kumar & D. Ghosh National Institute of Technology, Durgapur E-mail : Mohanrao.mamdikar@gmail.com,

More information

OPTIMAL CONTROL PROBLEM FOR PROCESSES REPRESENTED BY STOCHASTIC SEQUENTIAL MACHINE

OPTIMAL CONTROL PROBLEM FOR PROCESSES REPRESENTED BY STOCHASTIC SEQUENTIAL MACHINE OPTIMA CONTRO PROBEM FOR PROCESSES REPRESENTED BY STOCHASTIC SEQUENTIA MACHINE Yaup H. HACI an Muhammet CANDAN Department of Mathematics, Canaale Onseiz Mart University, Canaale, Turey ABSTRACT In this

More information

Fractional Geometric Calculus: Toward A Unified Mathematical Language for Physics and Engineering

Fractional Geometric Calculus: Toward A Unified Mathematical Language for Physics and Engineering Fractional Geometric Calculus: Towar A Unifie Mathematical Language for Physics an Engineering Xiong Wang Center of Chaos an Complex Network, Department of Electronic Engineering, City University of Hong

More information

Multi-robot Formation Control Using Reinforcement Learning Method

Multi-robot Formation Control Using Reinforcement Learning Method Multi-robot Formation Control Using Reinforcement Learning Metho Guoyu Zuo, Jiatong Han, an Guansheng Han School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing

More information

The derivative of a function f(x) is another function, defined in terms of a limiting expression: f(x + δx) f(x)

The derivative of a function f(x) is another function, defined in terms of a limiting expression: f(x + δx) f(x) Y. D. Chong (2016) MH2801: Complex Methos for the Sciences 1. Derivatives The erivative of a function f(x) is another function, efine in terms of a limiting expression: f (x) f (x) lim x δx 0 f(x + δx)

More information

Calculus of Variations

Calculus of Variations Calculus of Variations Lagrangian formalism is the main tool of theoretical classical mechanics. Calculus of Variations is a part of Mathematics which Lagrangian formalism is base on. In this section,

More information

Lecture 6: Calculus. In Song Kim. September 7, 2011

Lecture 6: Calculus. In Song Kim. September 7, 2011 Lecture 6: Calculus In Song Kim September 7, 20 Introuction to Differential Calculus In our previous lecture we came up with several ways to analyze functions. We saw previously that the slope of a linear

More information

Diophantine Approximations: Examining the Farey Process and its Method on Producing Best Approximations

Diophantine Approximations: Examining the Farey Process and its Method on Producing Best Approximations Diophantine Approximations: Examining the Farey Process an its Metho on Proucing Best Approximations Kelly Bowen Introuction When a person hears the phrase irrational number, one oes not think of anything

More information

Practical Computation of Flat Outputs for Nonlinear Control Systems

Practical Computation of Flat Outputs for Nonlinear Control Systems IOP onference Series: Materials Science an Engineering PPER OPEN ESS Practical omputation of Flat Outputs for Nonlinear ontrol Systems To cite this article: Joe Imae et al 25 IOP onf Ser: Mater Sci Eng

More information

Time-of-Arrival Estimation in Non-Line-Of-Sight Environments

Time-of-Arrival Estimation in Non-Line-Of-Sight Environments 2 Conference on Information Sciences an Systems, The Johns Hopkins University, March 2, 2 Time-of-Arrival Estimation in Non-Line-Of-Sight Environments Sinan Gezici, Hisashi Kobayashi an H. Vincent Poor

More information

Lagrangian and Hamiltonian Mechanics

Lagrangian and Hamiltonian Mechanics Lagrangian an Hamiltonian Mechanics.G. Simpson, Ph.. epartment of Physical Sciences an Engineering Prince George s Community College ecember 5, 007 Introuction In this course we have been stuying classical

More information

Pseudo-Free Families of Finite Computational Elementary Abelian p-groups

Pseudo-Free Families of Finite Computational Elementary Abelian p-groups Pseuo-Free Families of Finite Computational Elementary Abelian p-groups Mikhail Anokhin Information Security Institute, Lomonosov University, Moscow, Russia anokhin@mccme.ru Abstract We initiate the stuy

More information

2Algebraic ONLINE PAGE PROOFS. foundations

2Algebraic ONLINE PAGE PROOFS. foundations Algebraic founations. Kick off with CAS. Algebraic skills.3 Pascal s triangle an binomial expansions.4 The binomial theorem.5 Sets of real numbers.6 Surs.7 Review . Kick off with CAS Playing lotto Using

More information

4. Important theorems in quantum mechanics

4. Important theorems in quantum mechanics TFY4215 Kjemisk fysikk og kvantemekanikk - Tillegg 4 1 TILLEGG 4 4. Important theorems in quantum mechanics Before attacking three-imensional potentials in the next chapter, we shall in chapter 4 of this

More information

Counting Lattice Points in Polytopes: The Ehrhart Theory

Counting Lattice Points in Polytopes: The Ehrhart Theory 3 Counting Lattice Points in Polytopes: The Ehrhart Theory Ubi materia, ibi geometria. Johannes Kepler (1571 1630) Given the profusion of examples that gave rise to the polynomial behavior of the integer-point

More information

A Modification of the Jarque-Bera Test. for Normality

A Modification of the Jarque-Bera Test. for Normality Int. J. Contemp. Math. Sciences, Vol. 8, 01, no. 17, 84-85 HIKARI Lt, www.m-hikari.com http://x.oi.org/10.1988/ijcms.01.9106 A Moification of the Jarque-Bera Test for Normality Moawa El-Fallah Ab El-Salam

More information

Nonlinear Dielectric Response of Periodic Composite Materials

Nonlinear Dielectric Response of Periodic Composite Materials onlinear Dielectric Response of Perioic Composite aterials A.G. KOLPAKOV 3, Bl.95, 9 th ovember str., ovosibirsk, 639 Russia the corresponing author e-mail: agk@neic.nsk.su, algk@ngs.ru A. K.TAGATSEV Ceramics

More information

Diagonalization of Matrices Dr. E. Jacobs

Diagonalization of Matrices Dr. E. Jacobs Diagonalization of Matrices Dr. E. Jacobs One of the very interesting lessons in this course is how certain algebraic techniques can be use to solve ifferential equations. The purpose of these notes is

More information

We G Model Reduction Approaches for Solution of Wave Equations for Multiple Frequencies

We G Model Reduction Approaches for Solution of Wave Equations for Multiple Frequencies We G15 5 Moel Reuction Approaches for Solution of Wave Equations for Multiple Frequencies M.Y. Zaslavsky (Schlumberger-Doll Research Center), R.F. Remis* (Delft University) & V.L. Druskin (Schlumberger-Doll

More information

Fast image compression using matrix K-L transform

Fast image compression using matrix K-L transform Fast image compression using matrix K-L transform Daoqiang Zhang, Songcan Chen * Department of Computer Science an Engineering, Naning University of Aeronautics & Astronautics, Naning 2006, P.R. China.

More information

Convergence rates of moment-sum-of-squares hierarchies for optimal control problems

Convergence rates of moment-sum-of-squares hierarchies for optimal control problems Convergence rates of moment-sum-of-squares hierarchies for optimal control problems Milan Kora 1, Diier Henrion 2,3,4, Colin N. Jones 1 Draft of September 8, 2016 Abstract We stuy the convergence rate

More information

Necessary and Sufficient Conditions for Sketched Subspace Clustering

Necessary and Sufficient Conditions for Sketched Subspace Clustering Necessary an Sufficient Conitions for Sketche Subspace Clustering Daniel Pimentel-Alarcón, Laura Balzano 2, Robert Nowak University of Wisconsin-Maison, 2 University of Michigan-Ann Arbor Abstract This

More information

Leaving Randomness to Nature: d-dimensional Product Codes through the lens of Generalized-LDPC codes

Leaving Randomness to Nature: d-dimensional Product Codes through the lens of Generalized-LDPC codes Leaving Ranomness to Nature: -Dimensional Prouct Coes through the lens of Generalize-LDPC coes Tavor Baharav, Kannan Ramchanran Dept. of Electrical Engineering an Computer Sciences, U.C. Berkeley {tavorb,

More information

A Randomized Approximate Nearest Neighbors Algorithm - a short version

A Randomized Approximate Nearest Neighbors Algorithm - a short version We present a ranomize algorithm for the approximate nearest neighbor problem in - imensional Eucliean space. Given N points {x } in R, the algorithm attempts to fin k nearest neighbors for each of x, where

More information

Cascaded redundancy reduction

Cascaded redundancy reduction Network: Comput. Neural Syst. 9 (1998) 73 84. Printe in the UK PII: S0954-898X(98)88342-5 Cascae reunancy reuction Virginia R e Sa an Geoffrey E Hinton Department of Computer Science, University of Toronto,

More information

Math Notes on differentials, the Chain Rule, gradients, directional derivative, and normal vectors

Math Notes on differentials, the Chain Rule, gradients, directional derivative, and normal vectors Math 18.02 Notes on ifferentials, the Chain Rule, graients, irectional erivative, an normal vectors Tangent plane an linear approximation We efine the partial erivatives of f( xy, ) as follows: f f( x+

More information

Generalized Nonhomogeneous Abstract Degenerate Cauchy Problem

Generalized Nonhomogeneous Abstract Degenerate Cauchy Problem Applie Mathematical Sciences, Vol. 7, 213, no. 49, 2441-2453 HIKARI Lt, www.m-hikari.com Generalize Nonhomogeneous Abstract Degenerate Cauchy Problem Susilo Hariyanto Department of Mathematics Gajah Maa

More information

The Exact Form and General Integrating Factors

The Exact Form and General Integrating Factors 7 The Exact Form an General Integrating Factors In the previous chapters, we ve seen how separable an linear ifferential equations can be solve using methos for converting them to forms that can be easily

More information

On colour-blind distinguishing colour pallets in regular graphs

On colour-blind distinguishing colour pallets in regular graphs J Comb Optim (2014 28:348 357 DOI 10.1007/s10878-012-9556-x On colour-blin istinguishing colour pallets in regular graphs Jakub Przybyło Publishe online: 25 October 2012 The Author(s 2012. This article

More information

23 Implicit differentiation

23 Implicit differentiation 23 Implicit ifferentiation 23.1 Statement The equation y = x 2 + 3x + 1 expresses a relationship between the quantities x an y. If a value of x is given, then a corresponing value of y is etermine. For

More information

Approximate Molecular Orbital Calculations for H 2. George M. Shalhoub

Approximate Molecular Orbital Calculations for H 2. George M. Shalhoub Approximate Molecular Orbital Calculations for H + LA SALLE UNIVESITY 9 West Olney Ave. Philaelphia, PA 94 shalhoub@lasalle.eu Copyright. All rights reserve. You are welcome to use this ocument in your

More information

On a generalized combinatorial conjecture involving addition mod 2 k 1

On a generalized combinatorial conjecture involving addition mod 2 k 1 On a generalized combinatorial conjecture involving addition mod k 1 Gérard Cohen Jean-Pierre Flori Tuesday 14 th February, 01 Abstract In this note, e give a simple proof of the combinatorial conjecture

More information

arxiv: v1 [cs.it] 21 Aug 2017

arxiv: v1 [cs.it] 21 Aug 2017 Performance Gains of Optimal Antenna Deployment for Massive MIMO ystems Erem Koyuncu Department of Electrical an Computer Engineering, University of Illinois at Chicago arxiv:708.06400v [cs.it] 2 Aug 207

More information

Summary: Differentiation

Summary: Differentiation Techniques of Differentiation. Inverse Trigonometric functions The basic formulas (available in MF5 are: Summary: Differentiation ( sin ( cos The basic formula can be generalize as follows: Note: ( sin

More information

Some Classes of Invertible Matrices in GF(2)

Some Classes of Invertible Matrices in GF(2) Some Classes of Invertible Matrices in GF() James S. Plank Adam L. Buchsbaum Technical Report UT-CS-07-599 Department of Electrical Engineering and Computer Science University of Tennessee August 16, 007

More information

Lattices. A Lattice is a discrete subgroup of the additive group of n-dimensional space R n.

Lattices. A Lattice is a discrete subgroup of the additive group of n-dimensional space R n. Lattices A Lattice is a discrete subgroup of the additive group of n-dimensional space R n. Lattices have many uses in cryptography. They may be used to define cryptosystems and to break other ciphers.

More information

On the Inclined Curves in Galilean 4-Space

On the Inclined Curves in Galilean 4-Space Applie Mathematical Sciences Vol. 7 2013 no. 44 2193-2199 HIKARI Lt www.m-hikari.com On the Incline Curves in Galilean 4-Space Dae Won Yoon Department of Mathematics Eucation an RINS Gyeongsang National

More information

MATH , 06 Differential Equations Section 03: MWF 1:00pm-1:50pm McLaury 306 Section 06: MWF 3:00pm-3:50pm EEP 208

MATH , 06 Differential Equations Section 03: MWF 1:00pm-1:50pm McLaury 306 Section 06: MWF 3:00pm-3:50pm EEP 208 MATH 321-03, 06 Differential Equations Section 03: MWF 1:00pm-1:50pm McLaury 306 Section 06: MWF 3:00pm-3:50pm EEP 208 Instructor: Brent Deschamp Email: brent.eschamp@ssmt.eu Office: McLaury 316B Phone:

More information

Multi-View Clustering via Canonical Correlation Analysis

Multi-View Clustering via Canonical Correlation Analysis Keywors: multi-view learning, clustering, canonical correlation analysis Abstract Clustering ata in high-imensions is believe to be a har problem in general. A number of efficient clustering algorithms

More information

Analytic Scaling Formulas for Crossed Laser Acceleration in Vacuum

Analytic Scaling Formulas for Crossed Laser Acceleration in Vacuum October 6, 4 ARDB Note Analytic Scaling Formulas for Crosse Laser Acceleration in Vacuum Robert J. Noble Stanfor Linear Accelerator Center, Stanfor University 575 San Hill Roa, Menlo Park, California 945

More information

Discrete Hamilton Jacobi Theory and Discrete Optimal Control

Discrete Hamilton Jacobi Theory and Discrete Optimal Control 49th IEEE Conference on Decision an Control December 15-17, 2010 Hilton Atlanta Hotel, Atlanta, GA, USA Discrete Hamilton Jacobi Theory an Discrete Optimal Control Tomoi Ohsawa, Anthony M. Bloch, an Melvin

More information

One-dimensional I test and direction vector I test with array references by induction variable

One-dimensional I test and direction vector I test with array references by induction variable Int. J. High Performance Computing an Networking, Vol. 3, No. 4, 2005 219 One-imensional I test an irection vector I test with array references by inuction variable Minyi Guo School of Computer Science

More information

From Local to Global Control

From Local to Global Control Proceeings of the 47th IEEE Conference on Decision an Control Cancun, Mexico, Dec. 9-, 8 ThB. From Local to Global Control Stephen P. Banks, M. Tomás-Roríguez. Automatic Control Engineering Department,

More information

Pattern Propagation Speed in Synfire Chains with Excitatory- Inhibitory Couplings

Pattern Propagation Speed in Synfire Chains with Excitatory- Inhibitory Couplings Pattern Propagation Spee in Synfire Chains ith Excitatory- Inhibitory Couplings Baktash Babai School of Intelligent Systems, Institutes for Stuies in Theoretical Physics & Mathematics baktash@ipm.ir Abstract

More information

EE 418: Network Security and Cryptography

EE 418: Network Security and Cryptography Problem 1 EE 418: Network Security an Cryptography Homework 5 Assigne: Wenesay, November 23, 2016, Due: Tuesay, December 6, 2016 Instructor: Tamara Bonaci Department of Electrical Engineering University

More information

BEYOND THE CONSTRUCTION OF OPTIMAL SWITCHING SURFACES FOR AUTONOMOUS HYBRID SYSTEMS. Mauro Boccadoro Magnus Egerstedt Paolo Valigi Yorai Wardi

BEYOND THE CONSTRUCTION OF OPTIMAL SWITCHING SURFACES FOR AUTONOMOUS HYBRID SYSTEMS. Mauro Boccadoro Magnus Egerstedt Paolo Valigi Yorai Wardi BEYOND THE CONSTRUCTION OF OPTIMAL SWITCHING SURFACES FOR AUTONOMOUS HYBRID SYSTEMS Mauro Boccaoro Magnus Egerstet Paolo Valigi Yorai Wari {boccaoro,valigi}@iei.unipg.it Dipartimento i Ingegneria Elettronica

More information

Hybrid Fusion for Biometrics: Combining Score-level and Decision-level Fusion

Hybrid Fusion for Biometrics: Combining Score-level and Decision-level Fusion Hybri Fusion for Biometrics: Combining Score-level an Decision-level Fusion Qian Tao Raymon Velhuis Signals an Systems Group, University of Twente Postbus 217, 7500AE Enschee, the Netherlans {q.tao,r.n.j.velhuis}@ewi.utwente.nl

More information

Monte Carlo Methods with Reduced Error

Monte Carlo Methods with Reduced Error Monte Carlo Methos with Reuce Error As has been shown, the probable error in Monte Carlo algorithms when no information about the smoothness of the function is use is Dξ r N = c N. It is important for

More information

Influence of weight initialization on multilayer perceptron performance

Influence of weight initialization on multilayer perceptron performance Influence of weight initialization on multilayer perceptron performance M. Karouia (1,2) T. Denœux (1) R. Lengellé (1) (1) Université e Compiègne U.R.A. CNRS 817 Heuiasyc BP 649 - F-66 Compiègne ceex -

More information

A simplified macroscopic urban traffic network model for model-based predictive control

A simplified macroscopic urban traffic network model for model-based predictive control Delft University of Technology Delft Center for Systems an Control Technical report 9-28 A simplifie macroscopic urban traffic network moel for moel-base preictive control S. Lin, B. De Schutter, Y. Xi,

More information

Estimation of District Level Poor Households in the State of. Uttar Pradesh in India by Combining NSSO Survey and

Estimation of District Level Poor Households in the State of. Uttar Pradesh in India by Combining NSSO Survey and Int. Statistical Inst.: Proc. 58th Worl Statistical Congress, 2011, Dublin (Session CPS039) p.6567 Estimation of District Level Poor Househols in the State of Uttar Praesh in Inia by Combining NSSO Survey

More information

A Constructive Inversion Framework for Twisted Convolution

A Constructive Inversion Framework for Twisted Convolution A Constructive Inversion Framework for Twiste Convolution Yonina C. Elar, Ewa Matusiak, Tobias Werther June 30, 2006 Subject Classification: 44A35, 15A30, 42C15 Key Wors: Twiste convolution, Wiener s Lemma,

More information

On the Surprising Behavior of Distance Metrics in High Dimensional Space

On the Surprising Behavior of Distance Metrics in High Dimensional Space On the Surprising Behavior of Distance Metrics in High Dimensional Space Charu C. Aggarwal, Alexaner Hinneburg 2, an Daniel A. Keim 2 IBM T. J. Watson Research Center Yortown Heights, NY 0598, USA. charu@watson.ibm.com

More information

Formulation of statistical mechanics for chaotic systems

Formulation of statistical mechanics for chaotic systems PRAMANA c Inian Acaemy of Sciences Vol. 72, No. 2 journal of February 29 physics pp. 315 323 Formulation of statistical mechanics for chaotic systems VISHNU M BANNUR 1, an RAMESH BABU THAYYULLATHIL 2 1

More information

3.7 Implicit Differentiation -- A Brief Introduction -- Student Notes

3.7 Implicit Differentiation -- A Brief Introduction -- Student Notes Fin these erivatives of these functions: y.7 Implicit Differentiation -- A Brief Introuction -- Stuent Notes tan y sin tan = sin y e = e = Write the inverses of these functions: y tan y sin How woul we

More information

An Algebraic Approach to NTRU (q = 2 n ) via Witt Vectors and Overdetermined Systems of Nonlinear Equations

An Algebraic Approach to NTRU (q = 2 n ) via Witt Vectors and Overdetermined Systems of Nonlinear Equations An Algebraic Approach to NTRU (q = 2 n ) via Witt Vectors and Overdetermined Systems of Nonlinear Equations J.H. Silverman 1, N.P. Smart 2, and F. Vercauteren 2 1 Mathematics Department, Box 1917, Brown

More information

On the enumeration of partitions with summands in arithmetic progression

On the enumeration of partitions with summands in arithmetic progression AUSTRALASIAN JOURNAL OF COMBINATORICS Volume 8 (003), Pages 149 159 On the enumeration of partitions with summans in arithmetic progression M. A. Nyblom C. Evans Department of Mathematics an Statistics

More information

Bayesian Estimation of the Entropy of the Multivariate Gaussian

Bayesian Estimation of the Entropy of the Multivariate Gaussian Bayesian Estimation of the Entropy of the Multivariate Gaussian Santosh Srivastava Fre Hutchinson Cancer Research Center Seattle, WA 989, USA Email: ssrivast@fhcrc.org Maya R. Gupta Department of Electrical

More information