Vulnerability of MRD-Code-Based Universal Secure Error-Correcting Network Codes under Time-Varying Jamming Links
|
|
- Rudolph Moore
- 5 years ago
- Views:
Transcription
1 Vulnerability of MRD-Code-Based Universal Secure Error-Correcting Network Codes under Tie-Varying Jaing Links Jun Kurihara KDDI R&D Laboratories, Inc 2 5 Ohara, Fujiino, Saitaa, Japan Eail: kurihara@kddilabsjp Toohiko Uyeatsu Tokyo Institute of Technology 2 2 Ookayaa, Meguro, Tokyo, Japan Eail: uyeatsu@ieeeorg Abstract In order to provide reliable and secure counication against eavesdroppers and jaers over networks, Universal Secure Error-Correcting Network Codes (USECNC) based on Maxiu-Rank-Distance (MRD) codes have been introduced This code can be applied to any underlying network codes However, Shioji et al introduced a reasonable network odel against the code In their odel, an attacker eavesdrops inforation sybols fro soe links, where the set of eavesdropping links is re-selected during one packet transission The MRD-code-based USECNC cannot guarantee the security against eavesdroppers under this odel Inspired by Shioji et al s result, this paper considers the odel such that the set of links that jaing (error) sybols are injected into is re-selected for each tie slot We show that the MRDcode-based USECNC cannot guarantee the error-correcting capability under the odel of tie-varying jaing links, even if the nuber of jaing links is liited to only one Furtherore, by introducing a restriction on the field of local coding vectors in the network coding, we propose a siple solution to the proble of tie-varying jaing links for MRD-code-based USECNC Keywords-Network Coding; Secure Network Coding; Network Error-Correction; Jaing I INTRODUCTION Network coding [] has been attracting uch attentions since it can achieve better perforance than ordinary networks with routing ethods in ters of throughput, energy consuption, etc [2][3] In reality, network coding ay suffer fro two kinds of adversaries: eavesdropping and jaing Silva et al s presented Universal Secure Error-Correcting Network Codes (USECNC) [4][5] based on Maxiu-Rank-Distance (MRD) codes [6][7] to provide secure and reliable counication against eavesdroppers and jaers over the network Their code can be applied to any underlying network codes and hence it is called universal In the construction of Silva et al s USECNC, packets are split into segents and transitted to sink nodes through the network over tie slots It has been assued that packets are tapped by eavesdroppers and corrupted by jaers on several links that are fixed during one transission of packets, ie tie slots Shioji et al introduced the tie-varying eavesdroppinglink odel in which attacker can re-select the set of eavesdropping links at each tie slot of one packet transission [8] They showed that Silva et al s USECNC is insecure under this odel, that is, inforation is leaked to eavesdroppers [8] When the network coding is ipleented on an overlay network of the Internet, the assuption of tie-varying eavesdropping links is reasonable Although a packet is not split fro the perspective of the overlay network and they are transitted through fixed logical paths, a packet is split into ultiple fragents and they are routed over different physical paths at an interediate router Hence, fro the point of view of the overlay network, the set of tapped logical links ay be re-selected if physical links are eavesdropped Such attackers against the network ight be active, ie jaers who inject jaing packets into links to corrupt the network Thus, this paper considers the odel such that the attacker injects jaing (error) sybols into soe links (called jaing links ) in the network, and the set of jaing links is re-selected during one transission of packets We show that the MRD-code-based USECNC cannot guarantee the error-correcting capability under the odel of tie-varying jaing links, even if the nuber of jaing links is liited to only one Furtherore, by introducing a restriction on the field of local coding vectors (LCV s) in the network coding, we propose a siple solution to the proble of tie-varying jaing links for MRDcode-based USECNC The rest of this paper is organized as follows: Section II gives several definitions, the basic odel of network coding and a brief review of MRD-code-based USECNC presented by Silva et al In Section III, we introduce the tie-varying jaing-link odel, and show the vulnerability of MRDcode-based USECNC under this odel In Section IV, we propose a siple countereasure against this odel Finally, we conclude this paper in Section V II PRELIMINARIES In this section, we give definitions about the representation of finite field extensions, the basic odel of network coding Copyright (c) IARIA, 20 ISBN:
2 and a brief review of MRD-code-based USECNC presented by Silva et al A Field Extensions Let F q be a finite field containing q eleents, and let F q be an -degree field extension of a base field F q Then, F q can be viewed as a vector space over F q When the basis of the space is fixed, ie, an irreducible polynoial generating the field extension is deterined, an eleent of F q can be represented by an -diensional vector over F q We suppose that the vector representation of x F q is written by [, x (2),, x () ] F q B Network Coding Let G = (E, V) be a delay-free acyclic directed network, where E and V denote a set of links (edge, channel) and a set of nodes, respectively Let s V and R V respectively denote a source node and a set of sink nodes, where s R In this network odel, we suppose that each link can carry an eleent of F q per one tie slot The source node wishes to ulticast the sequence x = [x, x 2,, x n ] T F n q to all sink nodes at rate n, where the rate is defined as the nuber of eleents in F q transitted fro s per one tie slot Suppose that n in{axflow(s, r) : r R} holds, where axflow(s, r) (i, j V) denotes the ax-flow fro i to j Then, there exists a network coding ethod in which s can ulticast x to all nodes in R at rate equal to n [] We assue that linear network coding [9] is eployed over G, ie, the type of data processing perfored on the packets at each node is liited to linear cobination This iplies that the data flow on any link over G can be represented as an F q -linear cobination of the sequence x, x 2,, x n Thus, the inforation flow on link e E can be denoted as y e = b T e x using a global coding vector (GCV), b e = [b, b 2,, b n ] T F n q When one has access to, say, l links e, e 2,, e l, then the inforation obtained fro these links is denoted as M x F l q, where M = [ b e, b e2,, b en ] T Constructing a network code is equivalent to deterining the GCV of each link by setting the coefficients of the linear cobination perfored at each node C The MRD-Code-Based USECNC Here we introduce the fixed jaing-link odel and the construction of Silva et al s USECNC eployed over this odel [4][5] ) Fixed Jaing-Link Model: Suppose that one packet is coposed of an eleent of F q that is an -degree field extension of F q, and is represented by an -diensional vector over F q We define n packets transitted fro the source node s by X = [X, X 2,, X n ] T F n q (= F n q ) Then, the duration of one transission of X is coposed of tie slots The source node s splits X and sends the over tie slots using a linear network code though G, ie, transits [X (i), X(i) 2,, X(i) n ] T F n q at each tie slot i =,, Here we suppose that there exist t(< n) jaing links in E and they inject error packets into G Silva et al [4][5] assued that these links are fixed during one transission, ie, tie slots At a specific sink node, the received packets though G with injection of jaing (error) packets is represented by Y = AX + DZ F n q, () where A F n n q is a transition atrix corresponding to GCV s A linear network code eployed over G is called feasible [0] if rank A = n for all sink nodes, otherwise it is rank-deficient The rank deficiency is defined as ρ = n rank A On the other hand, Z F t q denotes t jaing (error) packets D F n t q is a transition atrix corresponding to jaing links D and Z are unknown rando variables with unknown distributions Then rank D t holds since t < n ust hold Throughout this paper, we focus our attention only on injection of jaing packets, and oit eavesdropping on links (cf, Shioji et al s analysis in [8]) 2) Silva et al s Schee: For the construction against the jaing(error)-packet injection, Silva et al s Universal Secure Error-Correcting Network Codes (USECNC) [4][5] is equivalent to [n, k] Maxiu Rank Distance (MRD) code C F n q satisfying n and 0 < k n 2t ρ, where ρ (= n rank A) is the rank deficiency of A Naely, the transitted packet X in Eq() is a codeword of C MRD code is a class of linear codes over F q, which is optial in the rank-distance sense For the [n, k] MRD code C, we have d R (C) 2t + ρ +, where d R (C) denotes the iniu rank-distance [6][7] between all pairs of distinct codewords of C We also have d R (AX, Y ) = rank (Y AX) = rank DZ rank Z t, (2) where d R (P, Q) denotes the rank-distance between atrices P, Q F n q Hence, the iniu rank-distance decoder of C can correct t jaing packets (and ρ rank deficiency of A) under the fixed jaing-link odel (n k 2t + ρ) [0] III TIME-VARYING JAMMING LINKS OVER THE NETWORK In this section, we introduce the odel of tie-varying jaing links, and show that MRD-code-based USECNC cannot guarantee the error-correcting capability under this odel Copyright (c) IARIA, 20 ISBN:
3 A The Model Suppose that the source node s ulticast n packets, ie, n vectors in F q, over tie slots i =, 2,, We consider a odel such that the jaer(s) can re-select the set of t jaing links at each tie slot i Denote the t error packets injected into G by a t atrix Z = [ z, z 2,, z ] F t q, where z i F t q (i =, 2,, ) is a t-diensional colun vector chosen according to arbitrary distribution At a specific sink node, let D i Fq n t (i =, 2,, ) be a n t atrix representing the linear cobination of jaing-packet segents at tie i Jaer(s) specify the set of jaing-links arbitrarily, and hence we assue that D i is chosen arbitrarily by jaer(s) The received atrix Y F n q at the specific sink node is written as Y = AX + V F n q, (3) where the atrix V denotes jaing packets conveyed over the tie-varying jaing links, given by V = [D z, D 2 z 2,, D z ] F n q When D = D 2 = = D, Eq(3) is equivalent to Silva et al s fixed jaing-link odel [4][5] B MRD-Code-Based USECNC under the Tie-Varying Jaing Link Model On the tie-varying jaing-link odel presented in the previous subsection, we first have the following lea Lea Suppose that the jaer(s) can arbitrarily select non-zero atrices D, D 2,, D fro F n t q and can generate non-zero vectors z, z 2,, z F t q Then the axiu possible rank of V is n Proof: It is only necessary to show an exaple having rank V = n Choose D i for i =, 2,, n (n ) as follows: D = [ u, 0,, 0 ], D 2 = [ u 2, 0,, 0 ], D n = [ u n, 0,, 0 ], where u j denotes the j-th colun vector of an n n identity atrix and 0 is a colun zero vector Let z i be represented as z i = [ i, 0,, 0] T F t q We assue that i is chosen fro F q = F q \{0} for i =,, n Then, we denote V by V = [D z, D 2 z 2,, D n z n D n+ z n+,, D + z + ] = [V V 2 ], where V can be represented as V = [D z, D 2 z 2,, D n z n ] 0 = 0 z n () We thus have rank V = n and hence rank V = n holds This copletes the lea Denote rank V = v We then have v n fro Lea Let X in Eq(3) be a codeword of Silva et al s USECNC, that is, [n, k] MRD code C with n and 0 < k n 2t ρ Fro Lea, jaers ay select D i and z i to satisfy v > t even if rank D i t holds for all i =,, We then have t < v = rank V = rank (Y AX) = d R (Y, AX) Thus, the decoder of C cannot correct t jaing (error) packets under the tie-varying jaing-link odel Moreover, we give the following lea that shows the nuber of jaing packets is independent of rank V Lea 2 Suppose that jaer(s) can arbitrarily choose D i s in Eq(3) fro F n t q Then, the axiu possible rank of V is n, which is independent of the value t > 0 Proof: Since the supposition n is required in Silva et al s USECNC, the exaple presented in the proof of Lea always satisfy rank V = n Moreover, in the exaple in the proof of Lea, we have assued rank D i = for i =, 2,, n independently fro the value t > 0 Thus the lea is copleted The above analysis proves the following result Theore Suppose that there are t jaing links in E under the tie-varying jaing-link odel Let the transitted packet over the network is a codeword of Silva et al s USECNC, ie, [n, k] MRD code C with 0 < k n 2t ρ and n Then, for any t > 0, the decoder of C cannot correct t jaing (error) packets injected fro tievarying jaing links This theore iplies that, under the tie-varying jaing-link odel, Silva et al s USECNC cannot guarantee the error-correction capability even if t = C An Exaple Assue q = 2, = n = 3 and t = We do not consider the rank deficiency and the existence of eavesdroppers, and set ρ = µ = 0 Fro these paraeters, the USECNC is defined as a [3, ] MRD code C F A codeword of C is defined as a 3 3 atrix X = [X, X 2, X 3 ] T C, where X i F 3 2 Copyright (c) IARIA, 20 ISBN:
4 Under the tie-varying jaing-link odel, a specific sink node receives the following packet Y = AX + V = A X X 2 + [D z, D 2 z 2, D 3 z 3 ], X 3 where A F 3 3 2, D i F 3 2 and z i F 2 for i =, 2, 3 Here we assue rank A = 3 and rank D i t = for i =, 2, 3 For exaple, we suppose A is a 3 3 identity atrix I and D i s are D = [, 0, 0] T, D 2 = [0,, 0] T, D 3 = [0, 0, ] T We note that rank D i = t = for each of i =, 2, 3 siilar to the fixed jaing-link odel We also represent X i = [X () i, X (2) i, X (3) i ] F 3 2 and z i = [z i ] F 2 for i =, 2, 3 We then receive X () X (2) X (3) z 0 0 Y = X () 2 X (2) 2 X (3) z 2 0 X () 3 X (2) 3 X (3) 0 0 z 3 3 In this case, we have rank V = 3 if all z, z 2, z 3 are nonzero and hence X cannot be uniquely reconstructed This can be viewed as the worst case of Silva et al s USECNC under the fixed jaing-link odel, that is, 3(> t) error packets are injected into the network IV A SIMPLE WAY TO PREVENT THE JAMMERS The siplest way to prevent tie-varying jaing links is to construct the network in which no packet is split into segents Naely, packets are transitted not as - diensional vectors using tie slots but as eleents of an -degree field extension itself at one tie slot in Silva et al s USECNC We thus consider how to transit codewords of [n, k] MRD code at one tie slot over a linear network code and how to decode the received codeword by the iniu rank-distance decoder Let p be a prie and the nuber of eleents in a base field F p And let p ( q) be the nuber of eleents in an extended field F p We redefine that each link can carry an eleent of F p per one tie slot The source node s wishes to transit x = [x, x 2,, x n ] T F n p to all sink nodes in R at one tie slot using a linear network code Then, the received packet at a specific sink node is represented as y = [y, y 2,, y n ] T = A x + D z F n p, (4) where A F n n p is a transition atrix according to a linear network code over F p z F t p denotes t jaing packets and D F t t p to jaing links is a transition atrix corresponding Assue x F n p(= Fn p ) in Eq(4) is a codeword of [n, k] MRD code defined over F p, where n and 0 < k n 2t + ρ Let M( ) denote the atrix representation of a vector in F n p to easure the rank distance between vectors For exaple, the atrix representation of x is M( x) = x (2) 2 x (2) x () 2 x () 2 Fn n x (2) n x () n p, where [ i, x (2) i,, x () i ] F p is the vector representation of x i F p (i =,, n) Then, according to Eq(4), we have d R (M(A x), M( y)) = rank (M( y) M(A x)) = rank (M( y A x)) = rank M(D z), (5) fro the properties of the additive operation over a field extension F p However, unlike Eq(2), rank M(D z) t does not always hold fro the property of the ultiplicative operation over F p Hence, the iniu rank-distance decoder of the [n, k] MRD code cannot be siply applied to Eq(4) Hence, we add the following condition to the construction of the underlying network coding such that rank M(D z) t always holds Condition For all nodes, eleents of local coding vectors (LCV s) are chosen fro the subfield (base field) F p of the field extension F p Note that eleents of LCV s are coefficients of linear cobination of incoing sybols at each node and they define eleents of GCV s [2] Thus, this condition also yields that the transition atrices A and D are defined over the subfield, ie, A F n n p F n n p and D Ft t p F t t p For any a F p and b F p, their vector representations are [0,, 0, a] F p and [b (), b (2),, b () ] F p, respectively Then, the ultiplication ab over F p is given by ab := [ab (), ab (2),, ab () ], fro the fact of finite fields That is, all operations can be executed over the base field F p Thus, under Condition, Copyright (c) IARIA, 20 ISBN:
5 we have d, d,2 d,t z d 2, d 2,2 d 2,t M(D z) = M z 2 d t, d t,2 d t,t z t w= d,wz w t w= = M 2,wz w w= d t,wz w w= [d,wz w (), d,w z w (2),, d,w z w () ] = w= [d 2,wz w (), d 2,w z w (2),, d 2,w z () w ] w= [d t,wz w (), d t,w z w (2),, d t,w z w () ] z () = D t z () t = DM( z), where d u,v F p (u, v =,, t) and [z w (), z w (2),, z w () ] F p is the vector representation of z w F p (w =,, t) Since rank M( z) t, we have rank M(D z) = rank DM( Z) t Cobining Eq(5) and this, the iniu rank-distance decoder of the [n, k] MRD code can decode the received packets y as with the Silva et al s USECNC for the fixed jaing-link odel However, this solution contains the following probles; a) The schee is not universal since the condition of LCV s is introduced b) Since the field for LCV s is changed fro F q to F p with p < q, the probability of rank deficiency becoes high when rando network coding [] is eployed, or the size of F p ight be insufficient for the deterinistic construction of network coding depending on the network structure [2] We believe the copletely different technique fro MRD codes ust be required to realize universal secure error-correcting codes against the tie-varying jaing links V CONCLUSION This paper considered the reasonable network odel such that the set of links that jaing (error) sybols are injected into is re-selected for each tie slot We revealed that the MRD-code-based USECNC cannot guarantee the errorcorrecting capability under the odel of tie-varying jaing links, even if the nuber of jaing links is liited to only one Further, by introducing a severe restriction to the field of LCV s, we presented a siple way to avoid jaing with the tie-varying jaing links for MRD-code-based USECNC REFERENCES [] R Ahlswede, N Cai, S-Y R Li, and R W Yeung, Network inforation flow, IEEE Transactions on Inforation Theory, vol 46, no 4, pp , 2000 [2] C Fragouli and E Soljanin, Network Coding Fundaentals Now Publishers, 2007 [3] C Fragouli and E Soljanin, Network Coding Applications Now Publishers, 2007 [4] D Silva and F R Kschischang, Universal secure errorcorrecting schees for network coding arxiv:003387v, Jan 200 Appeared in IEEE International Syposiu on Inforation Theory (ISIT) 200 Available at abs/003387v [5] D Silva and F R Kschischang, Universal secure network coding via rank-etric codes arxiv: v2, Apr 200 Available at [6] E M Gabidulin, Theory of codes with axiu rank distance, Probles of Inforation Transission, vol 2, no, pp 2, 985 [7] R M Roth, Maxiu-rank array codes and their application to crisscross error correction, IEEE Transactions on Inforation Theory, vol 37, no 2, pp , 99 [8] E Shioji, R Matsuoto, and T Uyeatsu, Vulnerability of MRD-code-based universal secure network coding against stronger eavesdroppers, IEICE Transactions on Fundaentals, vol E93-A, no, pp , 200 [9] S-Y R Li, R W Yeung, and N Cai, Linear network coding, IEEE Transactions on Inforation Theory, vol 49, no 2, pp 37 38, 2003 [0] D Silva and F R Kschischang, On etrics for error correction in network coding, IEEE Transactions on Inforation Theory, vol 55, no 2, pp , 2009 [] T Ho, M Médard, R Koetter, D R Karger, M Effros, J Shi, and B Leong, A rando linear network coding approach to ulticast, IEEE Transactions on Inforation Theory, vol 52, no 0, pp , 2006 [2] S Jaggi, P Sanders, P A Chou, M Effros, S Egner, K Jain, and L M G M Tolhuizen, Polynoial tie algoriths for ulticast network code construction, IEEE Transactions on Inforation Theory, vol 5, no 6, pp , 2005 Copyright (c) IARIA, 20 ISBN:
A Low-Complexity Congestion Control and Scheduling Algorithm for Multihop Wireless Networks with Order-Optimal Per-Flow Delay
A Low-Coplexity Congestion Control and Scheduling Algorith for Multihop Wireless Networks with Order-Optial Per-Flow Delay Po-Kai Huang, Xiaojun Lin, and Chih-Chun Wang School of Electrical and Coputer
More informationCompressive Sensing Over Networks
Forty-Eighth Annual Allerton Conference Allerton House, UIUC, Illinois, USA Septeber 29 - October, 200 Copressive Sensing Over Networks Soheil Feizi MIT Eail: sfeizi@it.edu Muriel Médard MIT Eail: edard@it.edu
More informationContent-Type Coding. arxiv: v2 [cs.it] 3 Jun Linqi Song UCLA
Content-Type Coding Linqi Song UCLA Eail: songlinqi@ucla.edu Christina Fragouli UCLA and EPFL Eail: christina.fragouli@ucla.edu arxiv:1505.03561v [cs.it] 3 Jun 015 Abstract This paper is otivated by the
More informationSupport recovery in compressed sensing: An estimation theoretic approach
Support recovery in copressed sensing: An estiation theoretic approach Ain Karbasi, Ali Horati, Soheil Mohajer, Martin Vetterli School of Coputer and Counication Sciences École Polytechnique Fédérale de
More informationOptimal Jamming Over Additive Noise: Vector Source-Channel Case
Fifty-first Annual Allerton Conference Allerton House, UIUC, Illinois, USA October 2-3, 2013 Optial Jaing Over Additive Noise: Vector Source-Channel Case Erah Akyol and Kenneth Rose Abstract This paper
More informationThis model assumes that the probability of a gap has size i is proportional to 1/i. i.e., i log m e. j=1. E[gap size] = i P r(i) = N f t.
CS 493: Algoriths for Massive Data Sets Feb 2, 2002 Local Models, Bloo Filter Scribe: Qin Lv Local Models In global odels, every inverted file entry is copressed with the sae odel. This work wells when
More informationA Simple Regression Problem
A Siple Regression Proble R. M. Castro March 23, 2 In this brief note a siple regression proble will be introduced, illustrating clearly the bias-variance tradeoff. Let Y i f(x i ) + W i, i,..., n, where
More informationSecret-Key Sharing Based on Layered Broadcast Coding over Fading Channels
Secret-Key Sharing Based on Layered Broadcast Coding over Fading Channels Xiaojun Tang WINLAB, Rutgers University Ruoheng Liu Princeton University Predrag Spasojević WINLAB, Rutgers University H. Vincent
More informationError Exponents in Asynchronous Communication
IEEE International Syposiu on Inforation Theory Proceedings Error Exponents in Asynchronous Counication Da Wang EECS Dept., MIT Cabridge, MA, USA Eail: dawang@it.edu Venkat Chandar Lincoln Laboratory,
More informationUsing EM To Estimate A Probablity Density With A Mixture Of Gaussians
Using EM To Estiate A Probablity Density With A Mixture Of Gaussians Aaron A. D Souza adsouza@usc.edu Introduction The proble we are trying to address in this note is siple. Given a set of data points
More informationDesign of Spatially Coupled LDPC Codes over GF(q) for Windowed Decoding
IEEE TRANSACTIONS ON INFORMATION THEORY (SUBMITTED PAPER) 1 Design of Spatially Coupled LDPC Codes over GF(q) for Windowed Decoding Lai Wei, Student Meber, IEEE, David G. M. Mitchell, Meber, IEEE, Thoas
More informationIterative Decoding of LDPC Codes over the q-ary Partial Erasure Channel
1 Iterative Decoding of LDPC Codes over the q-ary Partial Erasure Channel Rai Cohen, Graduate Student eber, IEEE, and Yuval Cassuto, Senior eber, IEEE arxiv:1510.05311v2 [cs.it] 24 ay 2016 Abstract In
More informationQuantum algorithms (CO 781, Winter 2008) Prof. Andrew Childs, University of Waterloo LECTURE 15: Unstructured search and spatial search
Quantu algoriths (CO 781, Winter 2008) Prof Andrew Childs, University of Waterloo LECTURE 15: Unstructured search and spatial search ow we begin to discuss applications of quantu walks to search algoriths
More informationMultiple Access Network Information-flow And Correction codes
Multiple Access Network Information-flow And Correction codes Hongyi Yao 1, Theodoros K. Dikaliotis, Sidharth Jaggi, Tracey Ho 1 Tsinghua University California Institute of Technology Chinese University
More informationInteractive Markov Models of Evolutionary Algorithms
Cleveland State University EngagedScholarship@CSU Electrical Engineering & Coputer Science Faculty Publications Electrical Engineering & Coputer Science Departent 2015 Interactive Markov Models of Evolutionary
More informationChapter 6 1-D Continuous Groups
Chapter 6 1-D Continuous Groups Continuous groups consist of group eleents labelled by one or ore continuous variables, say a 1, a 2,, a r, where each variable has a well- defined range. This chapter explores:
More informationDivisibility of Polynomials over Finite Fields and Combinatorial Applications
Designs, Codes and Cryptography anuscript No. (will be inserted by the editor) Divisibility of Polynoials over Finite Fields and Cobinatorial Applications Daniel Panario Olga Sosnovski Brett Stevens Qiang
More informationTight Bounds for Maximal Identifiability of Failure Nodes in Boolean Network Tomography
Tight Bounds for axial Identifiability of Failure Nodes in Boolean Network Toography Nicola Galesi Sapienza Università di Roa nicola.galesi@uniroa1.it Fariba Ranjbar Sapienza Università di Roa fariba.ranjbar@uniroa1.it
More informationA Self-Organizing Model for Logical Regression Jerry Farlow 1 University of Maine. (1900 words)
1 A Self-Organizing Model for Logical Regression Jerry Farlow 1 University of Maine (1900 words) Contact: Jerry Farlow Dept of Matheatics Univeristy of Maine Orono, ME 04469 Tel (07) 866-3540 Eail: farlow@ath.uaine.edu
More informationFinite-State Markov Modeling of Flat Fading Channels
International Telecounications Syposiu ITS, Natal, Brazil Finite-State Markov Modeling of Flat Fading Channels Cecilio Pientel, Tiago Falk and Luciano Lisbôa Counications Research Group - CODEC Departent
More informationNon-Parametric Non-Line-of-Sight Identification 1
Non-Paraetric Non-Line-of-Sight Identification Sinan Gezici, Hisashi Kobayashi and H. Vincent Poor Departent of Electrical Engineering School of Engineering and Applied Science Princeton University, Princeton,
More information13.2 Fully Polynomial Randomized Approximation Scheme for Permanent of Random 0-1 Matrices
CS71 Randoness & Coputation Spring 018 Instructor: Alistair Sinclair Lecture 13: February 7 Disclaier: These notes have not been subjected to the usual scrutiny accorded to foral publications. They ay
More informationFast Montgomery-like Square Root Computation over GF(2 m ) for All Trinomials
Fast Montgoery-like Square Root Coputation over GF( ) for All Trinoials Yin Li a, Yu Zhang a, a Departent of Coputer Science and Technology, Xinyang Noral University, Henan, P.R.China Abstract This letter
More informationThe Transactional Nature of Quantum Information
The Transactional Nature of Quantu Inforation Subhash Kak Departent of Coputer Science Oklahoa State University Stillwater, OK 7478 ABSTRACT Inforation, in its counications sense, is a transactional property.
More informationFixed-to-Variable Length Distribution Matching
Fixed-to-Variable Length Distribution Matching Rana Ali Ajad and Georg Böcherer Institute for Counications Engineering Technische Universität München, Gerany Eail: raa2463@gail.co,georg.boecherer@tu.de
More informationA Better Algorithm For an Ancient Scheduling Problem. David R. Karger Steven J. Phillips Eric Torng. Department of Computer Science
A Better Algorith For an Ancient Scheduling Proble David R. Karger Steven J. Phillips Eric Torng Departent of Coputer Science Stanford University Stanford, CA 9435-4 Abstract One of the oldest and siplest
More informationFeature Extraction Techniques
Feature Extraction Techniques Unsupervised Learning II Feature Extraction Unsupervised ethods can also be used to find features which can be useful for categorization. There are unsupervised ethods that
More informationIntelligent Systems: Reasoning and Recognition. Perceptrons and Support Vector Machines
Intelligent Systes: Reasoning and Recognition Jaes L. Crowley osig 1 Winter Seester 2018 Lesson 6 27 February 2018 Outline Perceptrons and Support Vector achines Notation...2 Linear odels...3 Lines, Planes
More informationPattern Recognition and Machine Learning. Artificial Neural networks
Pattern Recognition and Machine Learning Jaes L. Crowley ENSIMAG 3 - MMIS Fall Seester 2017 Lessons 7 20 Dec 2017 Outline Artificial Neural networks Notation...2 Introduction...3 Key Equations... 3 Artificial
More informationEfficient Filter Banks And Interpolators
Efficient Filter Banks And Interpolators A. G. DEMPSTER AND N. P. MURPHY Departent of Electronic Systes University of Westinster 115 New Cavendish St, London W1M 8JS United Kingdo Abstract: - Graphical
More informationOptimal Distributed Multicast Routing using Network Coding: Theory and Applications
1 Optial Distributed Multicast Routing using Network Coding: Theory and Applications Yi Cui, Yuan Xue, Klara Nahrstedt Departent of Coputer Science, University of Illinois at Urbana-Chapaign {yicui, xue,
More informationPhysics 215 Winter The Density Matrix
Physics 215 Winter 2018 The Density Matrix The quantu space of states is a Hilbert space H. Any state vector ψ H is a pure state. Since any linear cobination of eleents of H are also an eleent of H, it
More informationBirthday Paradox Calculations and Approximation
Birthday Paradox Calculations and Approxiation Joshua E. Hill InfoGard Laboratories -March- v. Birthday Proble In the birthday proble, we have a group of n randoly selected people. If we assue that birthdays
More informationStatistical clustering and Mineral Spectral Unmixing in Aviris Hyperspectral Image of Cuprite, NV
CS229 REPORT, DECEMBER 05 1 Statistical clustering and Mineral Spectral Unixing in Aviris Hyperspectral Iage of Cuprite, NV Mario Parente, Argyris Zynis I. INTRODUCTION Hyperspectral Iaging is a technique
More informationSupport Vector Machine Classification of Uncertain and Imbalanced data using Robust Optimization
Recent Researches in Coputer Science Support Vector Machine Classification of Uncertain and Ibalanced data using Robust Optiization RAGHAV PAT, THEODORE B. TRAFALIS, KASH BARKER School of Industrial Engineering
More informationBipartite subgraphs and the smallest eigenvalue
Bipartite subgraphs and the sallest eigenvalue Noga Alon Benny Sudaov Abstract Two results dealing with the relation between the sallest eigenvalue of a graph and its bipartite subgraphs are obtained.
More informationA note on the multiplication of sparse matrices
Cent. Eur. J. Cop. Sci. 41) 2014 1-11 DOI: 10.2478/s13537-014-0201-x Central European Journal of Coputer Science A note on the ultiplication of sparse atrices Research Article Keivan Borna 12, Sohrab Aboozarkhani
More informationControlled Flooding Search with Delay Constraints
Controlled Flooding Search with Delay Constraints Nicholas B. Chang and Mingyan Liu Departent of Electrical Engineering and Coputer Science University of Michigan, Ann Arbor, MI 4809-222 Eail: {changn,ingyan}@eecs.uich.edu
More informationSymbolic Analysis as Universal Tool for Deriving Properties of Non-linear Algorithms Case study of EM Algorithm
Acta Polytechnica Hungarica Vol., No., 04 Sybolic Analysis as Universal Tool for Deriving Properties of Non-linear Algoriths Case study of EM Algorith Vladiir Mladenović, Miroslav Lutovac, Dana Porrat
More informationThe Throughput-Outage Tradeoff of Wireless One-Hop Caching Networks
The Throughput-Outage Tradeoff of Wireless One-Hop Caching Networks Mingyue Ji, Student Meber, IEEE, Giuseppe Caire, Fellow, IEEE, and Andreas F. Molisch, Fellow, IEEE arxiv:3.637v [cs.it] 8 Jul 05 The
More informationPower-Controlled Feedback and Training for Two-way MIMO Channels
1 Power-Controlled Feedback and Training for Two-way MIMO Channels Vaneet Aggarwal, and Ashutosh Sabharwal, Senior Meber, IEEE arxiv:0901188v3 [csit] 9 Jan 010 Abstract Most counication systes use soe
More informationSupplementary Material for Fast and Provable Algorithms for Spectrally Sparse Signal Reconstruction via Low-Rank Hankel Matrix Completion
Suppleentary Material for Fast and Provable Algoriths for Spectrally Sparse Signal Reconstruction via Low-Ran Hanel Matrix Copletion Jian-Feng Cai Tianing Wang Ke Wei March 1, 017 Abstract We establish
More informationConvolutional Codes. Lecture Notes 8: Trellis Codes. Example: K=3,M=2, rate 1/2 code. Figure 95: Convolutional Encoder
Convolutional Codes Lecture Notes 8: Trellis Codes In this lecture we discuss construction of signals via a trellis. That is, signals are constructed by labeling the branches of an infinite trellis with
More informationFairness via priority scheduling
Fairness via priority scheduling Veeraruna Kavitha, N Heachandra and Debayan Das IEOR, IIT Bobay, Mubai, 400076, India vavitha,nh,debayan}@iitbacin Abstract In the context of ulti-agent resource allocation
More informationDistributed Subgradient Methods for Multi-agent Optimization
1 Distributed Subgradient Methods for Multi-agent Optiization Angelia Nedić and Asuan Ozdaglar October 29, 2007 Abstract We study a distributed coputation odel for optiizing a su of convex objective functions
More informationControlled Flooding Search with Delay Constraints
Controlled Flooding Search with Delay Constraints Nicholas B. Chang and Mingyan Liu Departent of Electrical Engineering and Coputer Science University of Michigan, Ann Arbor, MI 48109-2122 Eail: {changn,ingyan}@eecs.uich.edu
More informationAN APPLICATION OF INCOMPLETE BLOCK DESIGNS. K. J. C. Smith University of North Carolina. Institute of Statistics Mimeo Series No. 587.
AN APPLICATION OF INCOMPLETE BLOCK DESIGNS TO THE CONSTRUCTION OF ERROR-CORRECTING CODES by K. J. C. Sith University of North Carolina Institute of Statistics Mieo Series No. 587 August, 1968 This research
More informationRandomized Recovery for Boolean Compressed Sensing
Randoized Recovery for Boolean Copressed Sensing Mitra Fatei and Martin Vetterli Laboratory of Audiovisual Counication École Polytechnique Fédéral de Lausanne (EPFL) Eail: {itra.fatei, artin.vetterli}@epfl.ch
More informationLow-complexity, Low-memory EMS algorithm for non-binary LDPC codes
Low-coplexity, Low-eory EMS algorith for non-binary LDPC codes Adrian Voicila,David Declercq, François Verdier ETIS ENSEA/CP/CNRS MR-85 954 Cergy-Pontoise, (France) Marc Fossorier Dept. Electrical Engineering
More information60 CAPTER 6. INTRODUCTION TO BINARY BLOCK CODES Under the 2-PAM ap, the set (F 2 ) n of all binary n-tuples aps to the set of all real n-tuples of the
Chapter 6 Introduction to binary block codes In this chapter we b e g i n to study binary signal constellations, which are the Euclidean-space iages of binary block codes. Such constellations have bit
More informationOn the Communication Complexity of Lipschitzian Optimization for the Coordinated Model of Computation
journal of coplexity 6, 459473 (2000) doi:0.006jco.2000.0544, available online at http:www.idealibrary.co on On the Counication Coplexity of Lipschitzian Optiization for the Coordinated Model of Coputation
More informationIn this chapter, we consider several graph-theoretic and probabilistic models
THREE ONE GRAPH-THEORETIC AND STATISTICAL MODELS 3.1 INTRODUCTION In this chapter, we consider several graph-theoretic and probabilistic odels for a social network, which we do under different assuptions
More informationCompressive Distilled Sensing: Sparse Recovery Using Adaptivity in Compressive Measurements
1 Copressive Distilled Sensing: Sparse Recovery Using Adaptivity in Copressive Measureents Jarvis D. Haupt 1 Richard G. Baraniuk 1 Rui M. Castro 2 and Robert D. Nowak 3 1 Dept. of Electrical and Coputer
More informationarxiv: v1 [cs.ds] 17 Mar 2016
Tight Bounds for Single-Pass Streaing Coplexity of the Set Cover Proble Sepehr Assadi Sanjeev Khanna Yang Li Abstract arxiv:1603.05715v1 [cs.ds] 17 Mar 2016 We resolve the space coplexity of single-pass
More informationA MAXIMUM-LIKELIHOOD DECODER FOR JOINT PULSE POSITION AND AMPLITUDE MODULATIONS
The 18th Annual IEEE International Syposiu on Personal, Indoor and Mobile Radio Counications (PIMRC 07) A MAXIMUM-LIKELIHOOD DECODER FOR JOINT PULSE POSITION AND AMPLITUDE MODULATIONS Chadi Abou-Rjeily,
More informationExtension of CSRSM for the Parametric Study of the Face Stability of Pressurized Tunnels
Extension of CSRSM for the Paraetric Study of the Face Stability of Pressurized Tunnels Guilhe Mollon 1, Daniel Dias 2, and Abdul-Haid Soubra 3, M.ASCE 1 LGCIE, INSA Lyon, Université de Lyon, Doaine scientifique
More informationCurious Bounds for Floor Function Sums
1 47 6 11 Journal of Integer Sequences, Vol. 1 (018), Article 18.1.8 Curious Bounds for Floor Function Sus Thotsaporn Thanatipanonda and Elaine Wong 1 Science Division Mahidol University International
More informationBlock designs and statistics
Bloc designs and statistics Notes for Math 447 May 3, 2011 The ain paraeters of a bloc design are nuber of varieties v, bloc size, nuber of blocs b. A design is built on a set of v eleents. Each eleent
More informationA remark on a success rate model for DPA and CPA
A reark on a success rate odel for DPA and CPA A. Wieers, BSI Version 0.5 andreas.wieers@bsi.bund.de Septeber 5, 2018 Abstract The success rate is the ost coon evaluation etric for easuring the perforance
More information3.8 Three Types of Convergence
3.8 Three Types of Convergence 3.8 Three Types of Convergence 93 Suppose that we are given a sequence functions {f k } k N on a set X and another function f on X. What does it ean for f k to converge to
More informationImpact of Imperfect Channel State Information on ARQ Schemes over Rayleigh Fading Channels
This full text paper was peer reviewed at the direction of IEEE Counications Society subject atter experts for publication in the IEEE ICC 9 proceedings Ipact of Iperfect Channel State Inforation on ARQ
More informationWarning System of Dangerous Chemical Gas in Factory Based on Wireless Sensor Network
565 A publication of CHEMICAL ENGINEERING TRANSACTIONS VOL. 59, 07 Guest Editors: Zhuo Yang, Junie Ba, Jing Pan Copyright 07, AIDIC Servizi S.r.l. ISBN 978-88-95608-49-5; ISSN 83-96 The Italian Association
More informationCompression and Predictive Distributions for Large Alphabet i.i.d and Markov models
2014 IEEE International Syposiu on Inforation Theory Copression and Predictive Distributions for Large Alphabet i.i.d and Markov odels Xiao Yang Departent of Statistics Yale University New Haven, CT, 06511
More informationOn Poset Merging. 1 Introduction. Peter Chen Guoli Ding Steve Seiden. Keywords: Merging, Partial Order, Lower Bounds. AMS Classification: 68W40
On Poset Merging Peter Chen Guoli Ding Steve Seiden Abstract We consider the follow poset erging proble: Let X and Y be two subsets of a partially ordered set S. Given coplete inforation about the ordering
More informationarxiv: v1 [cs.ds] 29 Jan 2012
A parallel approxiation algorith for ixed packing covering seidefinite progras arxiv:1201.6090v1 [cs.ds] 29 Jan 2012 Rahul Jain National U. Singapore January 28, 2012 Abstract Penghui Yao National U. Singapore
More informationOn the Maximum Number of Codewords of X-Codes of Constant Weight Three
On the Maxiu Nuber of Codewords of X-Codes of Constant Weight Three arxiv:190.097v1 [cs.it] 2 Mar 2019 Yu Tsunoda Graduate School of Science and Engineering Chiba University 1- Yayoi-Cho Inage-Ku, Chiba
More informationAverage Consensus and Gossip Algorithms in Networks with Stochastic Asymmetric Communications
Average Consensus and Gossip Algoriths in Networks with Stochastic Asyetric Counications Duarte Antunes, Daniel Silvestre, Carlos Silvestre Abstract We consider that a set of distributed agents desire
More informationAsynchronous Gossip Algorithms for Stochastic Optimization
Asynchronous Gossip Algoriths for Stochastic Optiization S. Sundhar Ra ECE Dept. University of Illinois Urbana, IL 680 ssrini@illinois.edu A. Nedić IESE Dept. University of Illinois Urbana, IL 680 angelia@illinois.edu
More informationA Generalized Permanent Estimator and its Application in Computing Multi- Homogeneous Bézout Number
Research Journal of Applied Sciences, Engineering and Technology 4(23): 5206-52, 202 ISSN: 2040-7467 Maxwell Scientific Organization, 202 Subitted: April 25, 202 Accepted: May 3, 202 Published: Deceber
More informationList Scheduling and LPT Oliver Braun (09/05/2017)
List Scheduling and LPT Oliver Braun (09/05/207) We investigate the classical scheduling proble P ax where a set of n independent jobs has to be processed on 2 parallel and identical processors (achines)
More informationImplementing Non-Projective Measurements via Linear Optics: an Approach Based on Optimal Quantum State Discrimination
Ipleenting Non-Projective Measureents via Linear Optics: an Approach Based on Optial Quantu State Discriination Peter van Loock 1, Kae Neoto 1, Willia J. Munro, Philippe Raynal 2, Norbert Lütkenhaus 2
More informationSparse beamforming in peer-to-peer relay networks Yunshan Hou a, Zhijuan Qi b, Jianhua Chenc
3rd International Conference on Machinery, Materials and Inforation echnology Applications (ICMMIA 015) Sparse beaforing in peer-to-peer relay networs Yunshan ou a, Zhijuan Qi b, Jianhua Chenc College
More informationCOS 424: Interacting with Data. Written Exercises
COS 424: Interacting with Data Hoework #4 Spring 2007 Regression Due: Wednesday, April 18 Written Exercises See the course website for iportant inforation about collaboration and late policies, as well
More informationLecture 20 November 7, 2013
CS 229r: Algoriths for Big Data Fall 2013 Prof. Jelani Nelson Lecture 20 Noveber 7, 2013 Scribe: Yun Willia Yu 1 Introduction Today we re going to go through the analysis of atrix copletion. First though,
More informationRateless Codes for MIMO Channels
Rateless Codes for MIMO Channels Marya Modir Shanechi Dept EECS, MIT Cabridge, MA Eail: shanechi@itedu Uri Erez Tel Aviv University Raat Aviv, Israel Eail: uri@engtauacil Gregory W Wornell Dept EECS, MIT
More informationtime time δ jobs jobs
Approxiating Total Flow Tie on Parallel Machines Stefano Leonardi Danny Raz y Abstract We consider the proble of optiizing the total ow tie of a strea of jobs that are released over tie in a ultiprocessor
More informationMSEC MODELING OF DEGRADATION PROCESSES TO OBTAIN AN OPTIMAL SOLUTION FOR MAINTENANCE AND PERFORMANCE
Proceeding of the ASME 9 International Manufacturing Science and Engineering Conference MSEC9 October 4-7, 9, West Lafayette, Indiana, USA MSEC9-8466 MODELING OF DEGRADATION PROCESSES TO OBTAIN AN OPTIMAL
More informationData-Driven Imaging in Anisotropic Media
18 th World Conference on Non destructive Testing, 16- April 1, Durban, South Africa Data-Driven Iaging in Anisotropic Media Arno VOLKER 1 and Alan HUNTER 1 TNO Stieltjesweg 1, 6 AD, Delft, The Netherlands
More informationHybrid System Identification: An SDP Approach
49th IEEE Conference on Decision and Control Deceber 15-17, 2010 Hilton Atlanta Hotel, Atlanta, GA, USA Hybrid Syste Identification: An SDP Approach C Feng, C M Lagoa, N Ozay and M Sznaier Abstract The
More informationPattern Recognition and Machine Learning. Artificial Neural networks
Pattern Recognition and Machine Learning Jaes L. Crowley ENSIMAG 3 - MMIS Fall Seester 2016 Lessons 7 14 Dec 2016 Outline Artificial Neural networks Notation...2 1. Introduction...3... 3 The Artificial
More informationDevice-to-Device Collaboration through Distributed Storage
Device-to-Device Collaboration through Distributed Storage Negin Golrezaei, Student Meber, IEEE, Alexandros G Diakis, Meber, IEEE, Andreas F Molisch, Fellow, IEEE Dept of Electrical Eng University of Southern
More informationThe Role of Feedback in the choice between Routing and Coding for Wireless Unicast
The Role of Feedbac in the choice between Routing and Coding for Wireless Unicast Raarishna Guadi CSL and ECE, University of Illinois, Urbana, USA guadi2@illinois.edu Laurent Massoulie Thoson Corporate
More informationAlgorithms for parallel processor scheduling with distinct due windows and unit-time jobs
BULLETIN OF THE POLISH ACADEMY OF SCIENCES TECHNICAL SCIENCES Vol. 57, No. 3, 2009 Algoriths for parallel processor scheduling with distinct due windows and unit-tie obs A. JANIAK 1, W.A. JANIAK 2, and
More informationASSUME a source over an alphabet size m, from which a sequence of n independent samples are drawn. The classical
IEEE TRANSACTIONS ON INFORMATION THEORY Large Alphabet Source Coding using Independent Coponent Analysis Aichai Painsky, Meber, IEEE, Saharon Rosset and Meir Feder, Fellow, IEEE arxiv:67.7v [cs.it] Jul
More informationSolutions of some selected problems of Homework 4
Solutions of soe selected probles of Hoework 4 Sangchul Lee May 7, 2018 Proble 1 Let there be light A professor has two light bulbs in his garage. When both are burned out, they are replaced, and the next
More informationConstrained Consensus and Optimization in Multi-Agent Networks arxiv: v2 [math.oc] 17 Dec 2008
LIDS Report 2779 1 Constrained Consensus and Optiization in Multi-Agent Networks arxiv:0802.3922v2 [ath.oc] 17 Dec 2008 Angelia Nedić, Asuan Ozdaglar, and Pablo A. Parrilo February 15, 2013 Abstract We
More informationExperimental Design For Model Discrimination And Precise Parameter Estimation In WDS Analysis
City University of New York (CUNY) CUNY Acadeic Works International Conference on Hydroinforatics 8-1-2014 Experiental Design For Model Discriination And Precise Paraeter Estiation In WDS Analysis Giovanna
More informationA Smoothed Boosting Algorithm Using Probabilistic Output Codes
A Soothed Boosting Algorith Using Probabilistic Output Codes Rong Jin rongjin@cse.su.edu Dept. of Coputer Science and Engineering, Michigan State University, MI 48824, USA Jian Zhang jian.zhang@cs.cu.edu
More informationGeneralized Queries on Probabilistic Context-Free Grammars
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 1, JANUARY 1998 1 Generalized Queries on Probabilistic Context-Free Graars David V. Pynadath and Michael P. Wellan Abstract
More informationESTIMATING AND FORMING CONFIDENCE INTERVALS FOR EXTREMA OF RANDOM POLYNOMIALS. A Thesis. Presented to. The Faculty of the Department of Mathematics
ESTIMATING AND FORMING CONFIDENCE INTERVALS FOR EXTREMA OF RANDOM POLYNOMIALS A Thesis Presented to The Faculty of the Departent of Matheatics San Jose State University In Partial Fulfillent of the Requireents
More informationIEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 60, NO. 2, FEBRUARY ETSP stands for the Euclidean traveling salesman problem.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 60, NO., FEBRUARY 015 37 Target Assignent in Robotic Networks: Distance Optiality Guarantees and Hierarchical Strategies Jingjin Yu, Meber, IEEE, Soon-Jo Chung,
More informationThe Fundamental Basis Theorem of Geometry from an algebraic point of view
Journal of Physics: Conference Series PAPER OPEN ACCESS The Fundaental Basis Theore of Geoetry fro an algebraic point of view To cite this article: U Bekbaev 2017 J Phys: Conf Ser 819 012013 View the article
More informationA method to determine relative stroke detection efficiencies from multiplicity distributions
A ethod to deterine relative stroke detection eiciencies ro ultiplicity distributions Schulz W. and Cuins K. 2. Austrian Lightning Detection and Inoration Syste (ALDIS), Kahlenberger Str.2A, 90 Vienna,
More informationDESIGN OF THE DIE PROFILE FOR THE INCREMENTAL RADIAL FORGING PROCESS *
IJST, Transactions of Mechanical Engineering, Vol. 39, No. M1, pp 89-100 Printed in The Islaic Republic of Iran, 2015 Shira University DESIGN OF THE DIE PROFILE FOR THE INCREMENTAL RADIAL FORGING PROCESS
More informationOn Constant Power Water-filling
On Constant Power Water-filling Wei Yu and John M. Cioffi Electrical Engineering Departent Stanford University, Stanford, CA94305, U.S.A. eails: {weiyu,cioffi}@stanford.edu Abstract This paper derives
More informationA PROBABILISTIC AND RIPLESS THEORY OF COMPRESSED SENSING. Emmanuel J. Candès Yaniv Plan. Technical Report No November 2010
A PROBABILISTIC AND RIPLESS THEORY OF COMPRESSED SENSING By Eanuel J Candès Yaniv Plan Technical Report No 200-0 Noveber 200 Departent of Statistics STANFORD UNIVERSITY Stanford, California 94305-4065
More informationOn the Capacity of Secure Network Coding
On the Capacity of Secure Network Coding Jon Feldman Dept. of IEOR Tal Malkin Dept. of CS Rocco A. Servedio Dept. of CS Columbia University, New York, NY {jonfeld@ieor, tal@cs, rocco@cs, cliff@ieor}.columbia.edu
More informationSupplementary to Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
Suppleentary to Learning Discriinative Bayesian Networks fro High-diensional Continuous Neuroiaging Data Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, and Dinggang Shen Proposition. Given a sparse
More informationA1. Find all ordered pairs (a, b) of positive integers for which 1 a + 1 b = 3
A. Find all ordered pairs a, b) of positive integers for which a + b = 3 08. Answer. The six ordered pairs are 009, 08), 08, 009), 009 337, 674) = 35043, 674), 009 346, 673) = 3584, 673), 674, 009 337)
More informationSupplementary Information for Design of Bending Multi-Layer Electroactive Polymer Actuators
Suppleentary Inforation for Design of Bending Multi-Layer Electroactive Polyer Actuators Bavani Balakrisnan, Alek Nacev, and Elisabeth Sela University of Maryland, College Park, Maryland 074 1 Analytical
More information