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. The categorization algorithms described here are available in the ACT. Individual evaluation for each of the algorithms has been performed on the Reuters-21578 corpus. The results of the evaluations can be found in the “Performance results” subchapter.
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
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. 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. 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. Kadence is comprised of five principal algorithms for tuning (1) signal splits, (2) offsets, (3) cycle time, (4) phase sequence, and (5) time-of-day (XXX schedule). Second-by-second phase timing and detector data is polled from the controller, and new signal timing parameters are downloaded to field controllers every 3-4 cycles (minimum number of cycles is configurable by the user). The field controller then begins operating in an actuated-coordinated mode with the new settings.
Algorithms. 3.1. Kadence is comprised of five principle algorithms for tuning (1) signal splits, (2) offsets, (3) cycle time, (4) phase sequence, and (5) time-of-day (XXX schedule). Second-by-second phase timing and detector data is polled from the controller, and new signal timing parameters are downloaded to field controllers every 3-4 cycles (minimum number of cycles is configurable by the user). The field controller then begins operating in an actuated-coordinated mode with the new settings.
3.2. Kadence does not send hold or force-off commands to controllers, or suppress phase calls, so there is no risk of a controller getting stuck in a certain phase. All controller features operate normally including pedestrians, transit priority, and preemption. Kadence can run alongside an existing central system on an IP network using NTCIP or AB3418 protocols, depending on what is supported by the field device.
3.3. Kadence detects the presence of queues by measuring the average occupancy on a queue detector. When the level of occupancy is consistently high (a user-configurable threshold of occupancy) for several minutes (a user-configurable threshold of time), IF…THEN logic conditions can be configured to put Kadence into a variety of congestion management modes by selecting a new response coordination pattern with associated configuration parameters for Kadence to begin metering, increase cycle time, or change the coordination flow pattern.
3.4. All pedestrian functions are handled by the local controller. Kadence can be configured to allow split tuning to make the split lower than the pedestrian clearance times on a phase-by- phase basis, which results in a transition event if a pedestrian push button is activated, or can be configured to only allow splits that are larger than the pedestrian clearance time. Advance walk, delay walk, and all other pedestrian settings are handled by the field controller.
3.5. Kadence has a variety of configurable parameters to tailor the operation to the expectations of the City. Certain adjustments can be disallowed and some phases can be excluded from optimization by pattern. Configurable parameters include:
3.5.1. Exclude any phase from split tuning by pattern
3.5.2. Exclude or allow any lead-lag sequence by pattern
3.5.3. Exclude or allow cycle tuning by pattern
3.5.4. Exclude or allow offset tuning by pattern
3.5.5. Configure maximum deviation of splits from pattern values
3.5.6. Configure maximum deviation of offsets from pattern val...
Algorithms. Key Generation The algorithm GKE:KGen, on input the set of clients C and a security pa- rameter `, performs the following steps:
