{"component": "clause", "props": {"groups": [{"size": 6, "samples": [{"hash": "3auZFF7zr5a", "uri": "/contracts/3auZFF7zr5a#algorithms", "label": "Client Advisory Agreement", "score": 33.6448769308, "published": true}, {"hash": "iIvxsUhCSSP", "uri": "/contracts/iIvxsUhCSSP#algorithms", "label": "Client Advisory Agreement", "score": 33.5111455316, "published": true}, {"hash": "6hU5UUSrBrs", "uri": "/contracts/6hU5UUSrBrs#algorithms", "label": "Client Advisory Agreement", "score": 33.3082203414, "published": true}], "snippet": "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\u2019s 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.", "snippet_links": [{"key": "based-on", "type": "clause", "offset": [51, 59]}, {"key": "your-investment-objectives", "type": "clause", "offset": [60, 86]}, {"key": "model-portfolios", "type": "definition", "offset": [134, 150]}, {"key": "third-parties", "type": "clause", "offset": [186, 199]}, {"key": "the-third-party", "type": "definition", "offset": [221, 236]}, {"key": "reviewed-by", "type": "definition", "offset": [242, 253]}, {"key": "prior-to", "type": "definition", "offset": [269, 277]}, {"key": "the-portfolio", "type": "definition", "offset": [292, 305]}, {"key": "your-portfolio", "type": "clause", "offset": [434, 448]}, {"key": "number-of", "type": "clause", "offset": [506, 515]}, {"key": "tax-impact", "type": "clause", "offset": [531, 541]}, {"key": "the-investments", "type": "clause", "offset": [673, 688]}, {"key": "adjustments-to", "type": "clause", "offset": [698, 712]}, {"key": "risk-tolerance", "type": "definition", "offset": [745, 759]}, {"key": "business-reasons", "type": "definition", "offset": [847, 863]}, {"key": "at-any-time", "type": "clause", "offset": [1056, 1067]}, {"key": "portfolio-performance", "type": "clause", "offset": [1079, 1100]}], "hash": "72b42f1d07d0d48642d120c16caaa91d", "id": 1}, {"size": 3, "samples": [{"hash": "l4uDUpDCnQ6", "uri": "/contracts/l4uDUpDCnQ6#algorithms", "label": "License and Option Agreement (Orchid Biosciences Inc)", "score": 18.0, "published": true}, {"hash": "jeiNw1qLiAU", "uri": "/contracts/jeiNw1qLiAU#algorithms", "label": "License and Option Agreement (Orchid Biosciences Inc)", "score": 18.0, "published": true}, {"hash": "8aq1OLruHlv", "uri": "/contracts/8aq1OLruHlv#algorithms", "label": "License Agreement (Orchid Biosciences Inc)", "score": 18.0, "published": true}], "snippet": "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 \u2587\u2587\u2587\u2587\u2587\u2587\u2587 agreements with third parties.", "snippet_links": [{"key": "licensed-technology", "type": "clause", "offset": [22, 41]}, {"key": "data-mining", "type": "clause", "offset": [78, 89]}, {"key": "not-included", "type": "clause", "offset": [113, 125]}, {"key": "licenses-granted", "type": "clause", "offset": [133, 149]}, {"key": "subject-of", "type": "clause", "offset": [173, 183]}, {"key": "separate-agreement", "type": "clause", "offset": [186, 204]}, {"key": "subject-to", "type": "definition", "offset": [206, 216]}, {"key": "agreements-with-third-parties", "type": "clause", "offset": [229, 258]}], "hash": "1659e0ba24c2791f62b29388340f77b4", "id": 2}, {"size": 1, "samples": [{"hash": "7yT6QGnyhNc", "uri": "/contracts/7yT6QGnyhNc#algorithms", "label": "Distribution Agreement", "score": 24.2694325918, "published": true}], "snippet": "We have identified several well-known algorithms that can be adapted to work with for online learning in the presence of imbalanced data. An alternative to algorithm modification is through sampling strategies. But sampling is typically performed as a preprocess to classification, and it does not fit the online context.", "snippet_links": [{"key": "online-learning", "type": "clause", "offset": [86, 101]}, {"key": "in-the-presence-of", "type": "definition", "offset": [102, 120]}], "hash": "3aaf9bedb51115dcfdffe48ed9f156c5", "id": 3}, {"size": 1, "samples": [{"hash": "clwFN5Q7Wce", "uri": "/contracts/clwFN5Q7Wce#algorithms", "label": "Distribution Agreement", "score": 31.7314966153, "published": true}], "snippet": "In this section, we propose efficient greedy algorithms to approximate the optimiza- tion objectives for both G-STAC and L-STAC.", "snippet_links": [], "hash": "b7526388c4b7232a11adaf247edaa3a3", "id": 4}, {"size": 1, "samples": [{"hash": "Xg7Z9B0QjY", "uri": "/contracts/Xg7Z9B0QjY#algorithms", "label": "End User License Agreement", "score": 27.3423780017, "published": true}], "snippet": "We implemented \u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2019s algorithm [27] for performing FLASM under the Ham- ming distance model. The pseudocode for this is presented below in Algorithm 5. Let D\u2032[0 . . m][0 . . n] be a matrix, where D\u2032[i][j] contains the Hamming distance between some factor x[max{0, j \u2212 \u2113} . . j \u2212 1] of a string x and factor y[max{0, i \u2212 \u2113} . . i \u2212 1] of string y, for all 1 \u2264 j \u2264 n, 1 \u2264 i \u2264 m. The na\u00efve way to obtaining this matrix is through a straightforward O(m\u2113n)-time algorithm by constructing matrices Ds[0 . . \u2113][0 . . n], for all 0 \u2264 s \u2264 m \u2212 \u2113, where Ds[i][j] is the Hamming distance between some factor of x[j \u2212 \u2113 . . j \u2212 1] and the prefix of length i of y[s.. s + \u2113 \u2212 1]. We obtain D\u2032 by collating D0 and the last row of Ds, for all 0 \u2264 s \u2264 m \u2212 \u2113. Matrix Ds can be obtained using the standard dynamic programming algorithm. We say that x[max{0, i \u2212 \u2113} . . i \u2212 1] occurs in y ending at y[j \u2212 1] with k mismatches iff D\u2032[i][j] \u2264 k, for all 1 \u2264 i \u2264 m, 1 \u2264 j \u2264", "snippet_links": [{"key": "the-standard", "type": "clause", "offset": [781, 793]}], "hash": "01f0a2e7b8951efe9628bdd508bbbcfb", "id": 5}, {"size": 1, "samples": [{"hash": "6KawtOBt7GN", "uri": "/contracts/6KawtOBt7GN#algorithms", "label": "Adaptive Traffic Control System Agreement", "score": 26.6235687107, "published": true}], "snippet": "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 (\u2587\u2587\u2587 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.\n3.2. 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.\n3.3. 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\u2026THEN 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.\n3.4. 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.\n3.5. 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:\n3.5.1. Exclude any phase from split tuning by pattern\n3.5.2. Exclude or allow any lead-lag sequence by pattern\n3.5.3. Exclude or allow cycle tuning by pattern\n3.5.4. Exclude or allow offset tuning by pattern\n3.5.5. Configure maximum deviation of splits from pattern values\n3.5.6. Configure maximum deviation of offsets from pattern values\n3.5.7. Configure minimum and maximum cycle time\n3.5.8. Set phase minimum and maximum bounds by pattern, by phase\n3.5.9. Exclude or allow cycle selection\n3.5.10. Select any phases for biasing, by pattern", "snippet_links": [{"key": "comprised-of", "type": "clause", "offset": [16, 28]}, {"key": "cycle-time", "type": "definition", "offset": [102, 112]}, {"key": "second-phase", "type": "definition", "offset": [180, 192]}, {"key": "the-controller", "type": "clause", "offset": [233, 247]}, {"key": "number-of", "type": "clause", "offset": [344, 353]}, {"key": "by-the-user", "type": "clause", "offset": [377, 388]}, {"key": "the-field", "type": "definition", "offset": [391, 400]}, {"key": "no-risk", "type": "definition", "offset": [596, 603]}, {"key": "central-system", "type": "clause", "offset": [790, 804]}, {"key": "ip-network", "type": "definition", "offset": [811, 821]}, {"key": "congestion-management", "type": "clause", "offset": [1238, 1259]}, {"key": "a-phase", "type": "definition", "offset": [1627, 1634]}, {"key": "transition-event", "type": "clause", "offset": [1671, 1687]}, {"key": "push-button", "type": "definition", "offset": [1704, 1715]}, {"key": "clearance-time", "type": "definition", "offset": [1808, 1822]}, {"key": "the-operation", "type": "clause", "offset": [1985, 1998]}, {"key": "the-city", "type": "clause", "offset": [2022, 2030]}, {"key": "certain-adjustments", "type": "clause", "offset": [2032, 2051]}, {"key": "maximum-deviation", "type": "definition", "offset": [2390, 2407]}, {"key": "minimum-and-maximum", "type": "clause", "offset": [2521, 2540]}], "hash": "fbd0c41fd67f1f9358e5ea4c0bcdcd63", "id": 6}, {"size": 1, "samples": [{"hash": "1YK82zagFLF", "uri": "/contracts/1YK82zagFLF#algorithms", "label": "Multi Period Distribution Networks With Purchase Commitment Contracts", "score": 25.1875427789, "published": true}], "snippet": "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", "snippet_links": [{"key": "study-aims", "type": "clause", "offset": [18, 28]}, {"key": "the-performance", "type": "clause", "offset": [41, 56]}, {"key": "sections-6", "type": "clause", "offset": [148, 158]}], "hash": "8d386f7fabfd9f1f9a79c702dd99c30d", "id": 7}, {"size": 1, "samples": [{"hash": "6mV3bZUFrGy", "uri": "/contracts/6mV3bZUFrGy#algorithms", "label": "Grant Agreement", "score": 22.2566735113, "published": true}], "snippet": "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 \u201cPerformance results\u201d subchapter.", "snippet_links": [{"key": "the-act", "type": "clause", "offset": [62, 69]}, {"key": "results-of-the", "type": "clause", "offset": [172, 186]}, {"key": "performance-results", "type": "clause", "offset": [220, 239]}], "hash": "dce8b0aa8b998f0f161f299db2d4e178", "id": 8}, {"size": 1, "samples": [{"hash": "2xiiqMwg0X9", "uri": "/contracts/2xiiqMwg0X9#algorithms", "label": "Dissertation", "score": 21.8660238564, "published": true}], "snippet": "2.5.1.1 Setup A private key generator chooses a random number s \u2208 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.", "snippet_links": [{"key": "key-generator", "type": "definition", "offset": [24, 37]}, {"key": "system-parameters", "type": "clause", "offset": [130, 147]}, {"key": "master-key", "type": "clause", "offset": [203, 213]}], "hash": "26db42acd2ad6090d9eb0186143ed961", "id": 9}, {"size": 1, "samples": [{"hash": "j4Ca4tvQF3s", "uri": "/contracts/j4Ca4tvQF3s#algorithms", "label": "Nonfunded Cooperative Agreement", "score": 22.144421629, "published": true}], "snippet": "6.1 In the absence of a funded Cooperative Research and Development Agreement (CRADA) AIPL Algorithms developed solely by employee(s) of AIPL under this Agreement shall be owned by ARS and shall be made publicly available to others by ARS.\n6.2 In the absence of a funded Cooperative Research and Development Agreement (CRADA) AIPL Algorithms co-developed by employee(s) of ARS and COOPERATOR shall be co-owned by COOPERATOR and ARS and shall be made publicly available to others by ARS.\n6.3 Algorithms developed solely by employee(s) of COOPERATOR shall be owned by COOPERATOR and access to such COOPERATOR owned Algorithms will be determined by COOPERATOR.\n6.4 While this Agreement is in effect, AIPL has reasonable access to the Cooperator Database to generate and improve AIPL Algorithms for calculating genetic merit and for other non-commercial research purposes jointly agreed to by the parties consistent with this Agreement. Consistent with Article 2 (Confidentiality), ARS shall not release or disclose any information derived from the Cooperator Database including but not limited to individual animal genetic evaluations.", "snippet_links": [{"key": "cooperative-research-and-development-agreement", "type": "definition", "offset": [31, 77]}, {"key": "this-agreement", "type": "clause", "offset": [148, 162]}, {"key": "owned-by", "type": "definition", "offset": [172, 180]}, {"key": "available-to", "type": "definition", "offset": [212, 224]}, {"key": "cooperator-shall", "type": "clause", "offset": [381, 397]}, {"key": "in-effect", "type": "definition", "offset": [686, 695]}, {"key": "the-cooperator", "type": "clause", "offset": [727, 741]}, {"key": "to-generate", "type": "definition", "offset": [751, 762]}, {"key": "other-non", "type": "clause", "offset": [829, 838]}, {"key": "research-purposes", "type": "definition", "offset": [850, 867]}, {"key": "by-the-parties", "type": "clause", "offset": [886, 900]}, {"key": "consistent-with", "type": "clause", "offset": [901, 916]}, {"key": "article-2", "type": "clause", "offset": [949, 958]}, {"key": "not-limited", "type": "clause", "offset": [1079, 1090]}], "hash": "75a05de831a906cbd757fb446dc71749", "id": 10}], "next_curs": "ClMSTWoVc35sYXdpbnNpZGVyY29udHJhY3Rzci8LEhZDbGF1c2VTbmlwcGV0R3JvdXBfdjU2IhNhbGdvcml0aG1zIzAwMDAwMDBhDKIBAmVuGAAgAA==", "clause": {"size": 28, "title": "Algorithms", "children": [["decryption", "Decryption"], ["private-key-extraction", "Private Key Extraction"], ["execute-sign", "Execute SIGN"], ["the-server-s", "The server S"], ["centroid", "Centroid"]], "parents": [["grant-of-rights", "Grant of Rights"], ["licenses-to-orchid", "Licenses to Orchid"], ["algorithmic-procedure", "Algorithmic procedure"], ["identity-based-broadcast-encryption-scheme", "Identity-Based Broadcast Encryption Scheme"], ["protocol-preliminaries", "Protocol preliminaries"]], "id": "algorithms", "related": [["firmware", "Firmware", "Firmware"], ["processes", "Processes", "Processes"], ["techniques", "Techniques", "Techniques"], ["models", "Models", "Models"], ["architecture", "Architecture", "Architecture"]], "related_snippets": [], "updated": "2025-07-07T12:37:50+00:00"}, "json": true, "cursor": ""}}