203 research outputs found

    Fourier sparsity, spectral norm, and the Log-rank conjecture

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    We study Boolean functions with sparse Fourier coefficients or small spectral norm, and show their applications to the Log-rank Conjecture for XOR functions f(x\oplus y) --- a fairly large class of functions including well studied ones such as Equality and Hamming Distance. The rank of the communication matrix M_f for such functions is exactly the Fourier sparsity of f. Let d be the F2-degree of f and D^CC(f) stand for the deterministic communication complexity for f(x\oplus y). We show that 1. D^CC(f) = O(2^{d^2/2} log^{d-2} ||\hat f||_1). In particular, the Log-rank conjecture holds for XOR functions with constant F2-degree. 2. D^CC(f) = O(d ||\hat f||_1) = O(\sqrt{rank(M_f)}\logrank(M_f)). We obtain our results through a degree-reduction protocol based on a variant of polynomial rank, and actually conjecture that its communication cost is already \log^{O(1)}rank(M_f). The above bounds also hold for the parity decision tree complexity of f, a measure that is no less than the communication complexity (up to a factor of 2). Along the way we also show several structural results about Boolean functions with small F2-degree or small spectral norm, which could be of independent interest. For functions f with constant F2-degree: 1) f can be written as the summation of quasi-polynomially many indicator functions of subspaces with \pm-signs, improving the previous doubly exponential upper bound by Green and Sanders; 2) being sparse in Fourier domain is polynomially equivalent to having a small parity decision tree complexity; 3) f depends only on polylog||\hat f||_1 linear functions of input variables. For functions f with small spectral norm: 1) there is an affine subspace with co-dimension O(||\hat f||_1) on which f is a constant; 2) there is a parity decision tree with depth O(||\hat f||_1 log ||\hat f||_0).Comment: v2: Corollary 31 of v1 removed because of a bug in the proof. (Other results not affected.

    Implicit White-Box Implementations: White-Boxing ARX Ciphers

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    Since the first white-box implementation of AES published twenty years ago, no significant progress has been made in the design of secure implementations against an attacker with full control of the device. Designing white-box implementations of existing block ciphers is a challenging problem, as all proposals have been broken. Only two white-box design strategies have been published this far: the CEJO framework, which can only be applied to ciphers with small S-boxes, and self-equivalence encodings, which were only applied to AES. In this work we propose implicit implementations, a new design of white-box implementations based on implicit functions, and we show that current generic attacks that break CEJO or self-equivalence implementations are not successful against implicit implementations. The generation and the security of implicit implementations are related to the self-equivalences of the non-linear layer of the cipher, and we propose a new method to obtain self-equivalences based on the CCZ-equivalence. We implemented this method and many other functionalities in a new open-source tool BoolCrypt, which we used to obtain for the first time affine, linear, and even quadratic self-equivalences of the permuted modular addition. Using the implicit framework and these self-equivalences, we describe for the first time a practical white-box implementation of a generic Addition-Rotation-XOR (ARX) cipher, and we provide an open-source tool to easily generate implicit implementations of ARX ciphers

    ๊ธฐํ•˜ํ•™์ ์œผ๋กœ ์ •๋ฐ€ํ•œ ๋น„์„ ํ˜• ๊ตฌ์กฐ๋ฌผ์˜ ์•„์ด์†Œ-์ง€์˜ค๋ฉ”ํŠธ๋ฆญ ํ˜•์ƒ ์„ค๊ณ„ ๋ฏผ๊ฐ๋„ ํ•ด์„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์กฐ์„ ํ•ด์–‘๊ณตํ•™๊ณผ, 2019. 2. ์กฐ์„ ํ˜ธ.In this thesis, a continuum-based analytical adjoint configuration design sensitivity analysis (DSA) method is developed for gradient-based optimal design of curved built-up structures undergoing finite deformations. First, we investigate basic invariance property of linearized strain measures of a planar Timoshenko beam model which is combined with the selective reduced integration and B-bar projection method to alleviate shear and membrane locking. For a nonlinear structural analysis, geometrically exact beam and shell structural models are basically employed. A planar Kirchhoff beam problem is solved using the rotation-free discretization capability of isogeometric analysis (IGA) due to higher order continuity of NURBS basis function whose superior per-DOF(degree-of-freedom) accuracy over the conventional finite element analysis using Hermite basis function is verified. Various inter-patch continuity conditions including rotation continuity are enforced using Lagrage multiplier and penalty methods. This formulation is combined with a phenomenological constitutive model of shape memory polymer (SMP), and shape programming and recovery processes of SMP structures are simulated. Furthermore, for shear-deformable structures, a multiplicative update of finite rotations by an exponential map of a skew-symmetric matrix is employed. A procedure of explicit parameterization of local orthonormal frames in a spatial curve is presented using the smallest rotation method within the IGA framework. In the configuration DSA, the material derivative is applied to a variational equation, and an orientation design variation of curved structure is identified as a change of embedded local orthonormal frames. In a shell model, we use a regularized variational equation with a drilling rotational DOF. The material derivative of the orthogonal transformation matrix can be evaluated at final equilibrium configuration, which enables to compute design sensitivity using the tangent stiffness at the equilibrium without further iterations. A design optimization method for a constrained structure in a curved domain is also developed, which focuses on a lattice structure design on a specified surface. We define a lattice structure and its design variables on a rectangular plane, and utilize a concept of free-form deformation and a global curve interpolation to obtain an analytical expression for the control net of the structure on curved surface. The material derivative of the analytical expression eventually leads to precise design velocity field. Using this method, the number of design variables is reduced and design parameterization becomes more straightforward. In demonstrative examples, we verify the developed analytical adjoint DSA method in beam and shell structural problems undergoing finite deformations with various kinematic and force boundary conditions. The method is also applied to practical optimal design problems of curved built-up structures. For example, we extremize auxeticity of lattice structures, and experimentally verify nearly constant negative Poisson's ratio during large tensile and compressive deformations by using the 3-D printing and optical deformation measurement technologies. Also, we architect phononic band gap structures having significantly large band gap for mitigating noise in low audible frequency ranges.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋Œ€๋ณ€ํ˜•์„ ๊ณ ๋ คํ•œ ํœ˜์–ด์ง„ ์กฐ๋ฆฝ ๊ตฌ์กฐ๋ฌผ์˜ ์—ฐ์†์ฒด ๊ธฐ๋ฐ˜ ํ•ด์„์  ์• ์กฐ์ธ ํ˜•์ƒ ์„ค๊ณ„ ๋ฏผ๊ฐ๋„ ํ•ด์„ ๊ธฐ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ํ‰๋ฉด Timoshenko ๋น”์˜ ์„ ํ˜•ํ™”๋œ ๋ณ€ํ˜•๋ฅ ์˜ invariance ํŠน์„ฑ์„ ๊ณ ์ฐฐํ•˜์˜€๊ณ  invariant ์ •์‹ํ™”๋ฅผ ์„ ํƒ์  ์ถ•์†Œ์ ๋ถ„(selective reduced integration) ๊ธฐ๋ฒ• ๋ฐ B-bar projection ๊ธฐ๋ฒ•๊ณผ ๊ฒฐํ•ฉํ•˜์—ฌ shear ๋ฐ membrane ์ž ๊น€ ํ˜„์ƒ์„ ํ•ด์†Œํ•˜์˜€๋‹ค. ๋น„์„ ํ˜• ๊ตฌ์กฐ ๋ชจ๋ธ๋กœ์„œ ๊ธฐํ•˜ํ•™์ ์œผ๋กœ ์ •๋ฐ€ํ•œ ๋น” ๋ฐ ์‰˜ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ํ‰๋ฉด Kirchhoff ๋น” ๋ชจ๋ธ์„ NURBS ๊ธฐ์ €ํ•จ์ˆ˜์˜ ๊ณ ์ฐจ ์—ฐ์†์„ฑ์— ๋”ฐ๋ฅธ ์•„์ด์†Œ-์ง€์˜ค๋ฉ”ํŠธ๋ฆญ ํ•ด์„ ๊ธฐ๋ฐ˜ rotation-free ์ด์‚ฐํ™”๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋‹ค๋ฃจ์—ˆ์œผ๋ฉฐ, ๊ธฐ์กด์˜ Hermite ๊ธฐ์ €ํ•จ์ˆ˜ ๊ธฐ๋ฐ˜์˜ ์œ ํ•œ์š”์†Œ๋ฒ•์— ๋น„ํ•ด ์ž์œ ๋„๋‹น ํ•ด์˜ ์ •ํ™•๋„๊ฐ€ ๋†’์Œ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋ผ๊ทธ๋ž‘์ง€ ์Šน์ˆ˜๋ฒ• ๋ฐ ๋ฒŒ์น™ ๊ธฐ๋ฒ•์„ ๋„์ž…ํ•˜์—ฌ ํšŒ์ „์˜ ์—ฐ์†์„ฑ์„ ํฌํ•จํ•œ ๋‹ค์–‘ํ•œ ๋‹ค์ค‘ํŒจ์น˜๊ฐ„ ์—ฐ์† ์กฐ๊ฑด์„ ๊ณ ๋ คํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ๋ฒ•์„ ํ˜„์ƒํ•™์  (phenomenological) ํ˜•์ƒ๊ธฐ์–ตํด๋ฆฌ๋จธ (SMP) ์žฌ๋ฃŒ ๊ตฌ์„ฑ๋ฐฉ์ •์‹๊ณผ ๊ฒฐํ•ฉํ•˜์—ฌ ํ˜•์ƒ์˜ ํ”„๋กœ๊ทธ๋ž˜๋ฐ๊ณผ ํšŒ๋ณต ๊ณผ์ •์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•˜์˜€๋‹ค. ์ „๋‹จ๋ณ€ํ˜•์„ ๊ฒช๋Š” (shear-deformable) ๊ตฌ์กฐ ๋ชจ๋ธ์— ๋Œ€ํ•˜์—ฌ ๋Œ€ํšŒ์ „์˜ ๊ฐฑ์‹ ์„ ๊ต๋Œ€ ํ–‰๋ ฌ์˜ exponential map์— ์˜ํ•œ ๊ณฑ์˜ ํ˜•ํƒœ๋กœ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ณต๊ฐ„์ƒ์˜ ๊ณก์„  ๋ชจ๋ธ์—์„œ ์ตœ์†ŒํšŒ์ „ (smallest rotation) ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ๊ตญ์†Œ ์ •๊ทœ์ง๊ต์ขŒํ‘œ๊ณ„์˜ ๋ช…์‹œ์  ๋งค๊ฐœํ™”๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ํ˜•์ƒ ์„ค๊ณ„ ๋ฏผ๊ฐ๋„ ํ•ด์„์„ ์œ„ํ•˜์—ฌ ์ „๋ฏธ๋ถ„์„ ๋ณ€๋ถ„ ๋ฐฉ์ •์‹์— ์ ์šฉํ•˜์˜€์œผ๋ฉฐ ํœ˜์–ด์ง„ ๊ตฌ์กฐ๋ฌผ์˜ ๋ฐฐํ–ฅ ์„ค๊ณ„ ๋ณ€ํ™”๋Š” ๊ตญ์†Œ ์ •๊ทœ์ง๊ต์ขŒํ‘œ๊ณ„์˜ ํšŒ์ „์— ์˜ํ•˜์—ฌ ๊ธฐ์ˆ ๋œ๋‹ค. ์ตœ์ข… ๋ณ€ํ˜• ํ˜•์ƒ์—์„œ ์ง๊ต ๋ณ€ํ™˜ ํ–‰๋ ฌ์˜ ์ „๋ฏธ๋ถ„์„ ๊ณ„์‚ฐํ•จ์œผ๋กœ์จ ๋Œ€ํšŒ์ „ ๋ฌธ์ œ์—์„œ ์ถ”๊ฐ€์ ์ธ ๋ฐ˜๋ณต ๊ณ„์‚ฐ์—†์ด ๋ณ€ํ˜• ํ•ด์„์—์„œ์˜ ์ ‘์„ ๊ฐ•์„ฑํ–‰๋ ฌ์— ์˜ํ•ด ํ•ด์„์  ์„ค๊ณ„ ๋ฏผ๊ฐ๋„๋ฅผ ๊ณ„์‚ฐํ•  ์ˆ˜ ์žˆ๋‹ค. ์‰˜ ๊ตฌ์กฐ๋ฌผ์˜ ๊ฒฝ์šฐ ๋ฉด๋‚ด ํšŒ์ „ ์ž์œ ๋„ ๋ฐ ์•ˆ์ •ํ™”๋œ ๋ณ€๋ถ„ ๋ฐฉ์ •์‹์„ ํ™œ์šฉํ•˜์—ฌ ๋ณด๊ฐ•์žฌ(stiffener)์˜ ๋ชจ๋ธ๋ง์„ ์šฉ์ดํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํœ˜์–ด์ง„ ์˜์—ญ์— ๊ตฌ์†๋˜์–ด์žˆ๋Š” ๊ตฌ์กฐ๋ฌผ์— ๋Œ€ํ•œ ์„ค๊ณ„ ์†๋„์žฅ ๊ณ„์‚ฐ ๋ฐ ์ตœ์  ์„ค๊ณ„๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜๋ฉฐ ํŠนํžˆ ๊ณก๋ฉด์— ๊ตฌ์†๋œ ๋น” ๊ตฌ์กฐ๋ฌผ์˜ ์„ค๊ณ„๋ฅผ ์ง‘์ค‘์ ์œผ๋กœ ๋‹ค๋ฃฌ๋‹ค. ์ž์œ ํ˜•์ƒ๋ณ€ํ˜•(Free-form deformation)๊ธฐ๋ฒ•๊ณผ ์ „์—ญ ๊ณก์„  ๋ณด๊ฐ„๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ์ง์‚ฌ๊ฐ ํ‰๋ฉด์—์„œ ํ˜•์ƒ ๋ฐ ์„ค๊ณ„ ๋ณ€์ˆ˜๋ฅผ ์ •์˜ํ•˜๊ณ  ๊ณก๋ฉด์ƒ์˜ ๊ณก์„  ํ˜•์ƒ์„ ๋‚˜ํƒ€๋‚ด๋Š” ์กฐ์ •์  ์œ„์น˜๋ฅผ ํ•ด์„์ ์œผ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ด์˜ ์ „๋ฏธ๋ถ„์„ ํ†ตํ•ด ์ •ํ™•ํ•œ ์„ค๊ณ„์†๋„์žฅ์„ ๊ณ„์‚ฐํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์„ค๊ณ„ ๋ณ€์ˆ˜์˜ ๊ฐœ์ˆ˜๋ฅผ ์ค„์ผ ์ˆ˜ ์žˆ๊ณ  ์„ค๊ณ„์˜ ๋งค๊ฐœํ™”๊ฐ€ ๊ฐ„ํŽธํ•ด์ง„๋‹ค. ๊ฐœ๋ฐœ๋œ ๋ฐฉ๋ฒ•๋ก ์€ ๋‹ค์–‘ํ•œ ํ•˜์ค‘ ๋ฐ ์šด๋™ํ•™์  ๊ฒฝ๊ณ„์กฐ๊ฑด์„ ๊ฐ–๋Š” ๋น”๊ณผ ์‰˜์˜ ๋Œ€๋ณ€ํ˜• ๋ฌธ์ œ๋ฅผ ํ†ตํ•ด ๊ฒ€์ฆ๋˜๋ฉฐ ์—ฌ๋Ÿฌ๊ฐ€์ง€ ํœ˜์–ด์ง„ ์กฐ๋ฆฝ ๊ตฌ์กฐ๋ฌผ์˜ ์ตœ์  ์„ค๊ณ„์— ์ ์šฉ๋œ๋‹ค. ๋Œ€ํ‘œ์ ์œผ๋กœ, ์ „๋‹จ ๊ฐ•์„ฑ ๋ฐ ์ถฉ๊ฒฉ ํก์ˆ˜ ํŠน์„ฑ๊ณผ ๊ฐ™์€ ๊ธฐ๊ณ„์  ๋ฌผ์„ฑ์น˜์˜ ๊ฐœ์„ ์„ ์œ„ํ•ด ํ™œ์šฉ๋˜๋Š” ์˜ค๊ทธ์ œํ‹ฑ (auxetic) ํŠน์„ฑ์ด ๊ทน๋Œ€ํ™”๋œ ๊ฒฉ์ž ๊ตฌ์กฐ๋ฅผ ์„ค๊ณ„ํ•˜๋ฉฐ ์ธ์žฅ ๋ฐ ์••์ถ• ๋Œ€๋ณ€ํ˜• ๋ชจ๋‘์—์„œ ์ผ์ •ํ•œ ์Œ์˜ ํฌ์•„์†ก๋น„๋ฅผ ๋‚˜ํƒ€๋ƒ„์„ 3์ฐจ์› ํ”„๋ฆฐํŒ…๊ณผ ๊ด‘ํ•™์  ๋ณ€ํ˜• ์ธก์ • ๊ธฐ์ˆ ์„ ์ด์šฉํ•˜์—ฌ ์‹คํ—˜์ ์œผ๋กœ ๊ฒ€์ฆํ•œ๋‹ค. ๋˜ํ•œ ์šฐ๋ฆฌ๋Š” ์†Œ์Œ์˜ ์ €๊ฐ์„ ์œ„ํ•ด ํ™œ์šฉ๋˜๋Š” ๊ฐ€์ฒญ ์ €์ฃผํŒŒ์ˆ˜ ์˜์—ญ๋Œ€์—์„œ์˜ ๋ฐด๋“œ๊ฐญ์ด ๊ทน๋Œ€ํ™”๋œ ๊ฒฉ์ž ๊ตฌ์กฐ๋ฅผ ์ œ์‹œํ•œ๋‹ค.Abstract 1. Introduction 2. Isogeometric analysis of geometrically exact nonlinear structures 3. Isogeometric confinguration DSA of geometrically exact nonlinear structures 4. Numerical examples 5. Conclusions and future works A. Supplements to the geometrically exact Kirchhoff beam model B. Supplements to the geometrically exact shear-deformable beam model C. Supplements to the geometrically exact shear-deformable shell model D. Supplements to the invariant formulations E. Supplements to the geometric constraints in design optimization F. Supplements to the design of auxetic structures ์ดˆ๋กDocto

    Abstract Algebra: Theory and Applications

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    Tom Judson\u27s Abstract Algebra: Theory and Applications is an open source textbook designed to teach the principles and theory of abstract algebra to college juniors and seniors in a rigorous manner. Its strengths include a wide range of exercises, both computational and theoretical, plus many nontrivial applications. Rob Beezer has contributed complementary material using the open source system, Sage.An HTML version on the PreText platform is available here. The first half of the book presents group theory, through the Sylow theorems, with enough material for a semester-long course. The second-half is suitable for a second semester and presents rings, integral domains, Boolean algebras, vector spaces, and fields, concluding with Galois Theory.https://scholarworks.sfasu.edu/ebooks/1022/thumbnail.jp
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