Algorithms Sample Clauses

Algorithms. Revolut Wealth provides you with a model portfolio based on your investment objectives as outlined in the Questionnaire you complete. Model portfolios are generated by Revolut Wealth or third parties and, if generated by the third party, are reviewed by Revolut Wealth prior to recommending. The portfolio is managed via automatic portfolio rebalancing based on Revolut Wealth’s internal algorithms and is designed to reasonably keep your portfolio balanced within certain thresholds, while minimizing the number of rebalances and tax impact. If your portfolio deviates from the initial parameters due to market moves or otherwise, our algorithms will periodically monitor the investments and make adjustments to stay within your initial stated risk tolerance. Rebalancing on a particular date can fail for a variety of technical, operational, or business reasons, which can result in potential losses. Revolut Wealth will monitor algorithmic performance and will correct any failed rebalancing. Revolut Wealth will amend the specific algorithm parameters at any time to enhance portfolio performance and risk. Revolut Wealth may also unilaterally exercise its discretion to rebalance a portfolio.

Algorithms. Except as included in Licensed Technology, access by Orchid to algorithms for data mining and for informatics is not included in the licenses granted herein, but may be the subject of a separate agreement, subject to any Xxxxxxx agreements with third parties.

Algorithms. C C 1. Run SIGN.KGen(1l) for each client Ui in to provide each client with a pair (SKi, PKi) of signing/verifying keys; q 2. Choose x R Z٨ and set the Server’s private/public keys to be: (SKS, PKS) = (x, gx). One denotes y = gx. Setup The algorithm GKE.Setup, on input a set of client-devices , performs the following steps (see also Figure 1): 1. Set the wireless client group c to be the input set . 2. Each client Ui c chooses at random a value xi Zq and precomputes yi = gxi , αi = S PKxi = yxi as well as a signature σi of yi, under the private key SKi. 3. Each client Ui sends (yi, σi) to S. computes the values αi = yx. 4. For each i ∈ Gc, the server S checks the signature σi using PKi, and if they are all correct, 5. The server S initializes the counter c = 0, as a bit-string of length l1 and computes the shared secret value: K = H0(c {αi}i∈Gc ) and sends to each client Ui the values c and Ki = K ⊕ H1(c αi). 6. Each client Ui (and S) recovers the shared secret value K and the session key sk as described below, and accepts: K = Ki ⊕ H1(c αi) and sk = H(K Gc S). Gc = {1, 3} c', K1' = K' ⊕ H1(c' α1) Increases c into c' K' = H0(c' {αi}i∈Gc ) c', K3' = K' ⊕ H1(c' α3' ) K' = K1' ⊕ H1(c' α1) K' = K3' ⊕ H1(c' α3) Shared session key sk' = H(K' Gc S) Client U3 α3 c' > c? Client U1 α1 c' > c?

Algorithms. 2.5.1.1 Setup A private key generator chooses a random number s ∈ Zq* and set Ppub = sP. Then the private key generator publishes system parameters params = {G1, G2, q, P, Ppub, H1, H2}, and keep s as a master key.

Algorithms. 7.3.1. There is no anticipated requirement for the use of GCHQ cipher suites or KeyMat. 7.3.2. Service Providers will be required to have a fully operational and auditable key management process, including disaster recovery capabilities. 7.3.3. Use of quantum cryptography resistant ciphers will not be required for rehearsal operations and is not currently expected to require for 2021 live operations. 7.3.4. SSL / TLS. a. Systems using TLS/SSL must be able to support TLS1.2. b. Support for TLS1.3 is must be incorporated in all development or infrastructure plans. c. Support for earlier variants of TLS or SSL must be disabled. 7.3.5. Cipher Suites. a. Desirable: AES256/SHA-256 b. Minimum: AES-128/SHA-256. a. Use of Elliptic Curve ciphers is permitted. b. Key length and other protocol parameters for specific proposed use cases will be available via the Security Working Group.

Algorithms. We implemented Xxxxxxxxxx’s algorithm [27] for performing FLASM under the Ham- ming distance model. The pseudocode for this is presented below in Algorithm 5. Let D′[0 . . m][0 . . n] be a matrix, where D′[i][j] contains the Hamming distance between some factor x[max{0, j − ℓ} . . j − 1] of a string x and factor y[max{0, i − ℓ} . . i − 1] of string y, for all 1 ≤ j ≤ n, 1 ≤ i ≤ m. The naïve way to obtaining this matrix is through a straightforward O(mℓn)-time algorithm by constructing matrices Ds[0 . . ℓ][0 . . n], for all 0 ≤ s ≤ m − ℓ, where Ds[i][j] is the Hamming distance between some factor of x[j − ℓ . . j − 1] and the prefix of length i of y[s.. s + ℓ − 1]. We obtain D′ by collating D0 and the last row of Ds, for all 0 ≤ s ≤ m − ℓ. Matrix Ds can be obtained using the standard dynamic programming algorithm. We say that x[max{0, i − ℓ} . . i − 1] occurs in y ending at y[j − 1] with k mismatches iff D′[i][j] ≤ k, for all 1 ≤ i ≤ m, 1 ≤ j ≤

Algorithms. In this section, we propose efficient greedy algorithms to approximate the optimiza- tion objectives for both G-STAC and L-STAC.

Algorithms. Key Generation The algorithm GKE:KGen, on input the set of clients C and a security pa- rameter `, performs the following steps:

Algorithms. The computational study aims to evaluate the performance of different algorithms for solving the MDPC model. We compare the algorithms described in Sections 6 and 7 with two state-of-the- art generic algorithms, namely: CPLEX default implementations of branch-and-cut and of Benders decomposition. In contrast with our combinatorial Benders decomposition approach, CPLEX Benders decomposition classically separates the integer variables (αt , vt ), which are included in the master problem, from the continuous variables (qt ), which are handled in the LP sub-problem. Thus, we consider four methods, respectively labeled as: (CP-B&C) - CPLEX Branch-and-Cut; (CP-Bend) - CPLEX Benders; (CBA) - Combinatorial Benders Decomposition Algorithm (Section 6); (XX-XX) - Relax-and-repair heuristic (Section 7).

Algorithms. The computational study aims to evaluate the performance of different algorithms for solving the MDPC model. We compare the algorithms described in Sections 6 and 7 with two state-of-the- art generic algorithms, namely: CPLEX default implementations of branch-and-cut and of Benders