Table 5. Appropriate Legislative Provisions for Further Processing Specify the appropriate legal provision for further processing based on the following:
Table 5. Allowances * These allowances increase by 2% on the first full pay period on or after 1/7/2022 and 1/7/2023. Schedule A – Nursing Classification Definitions Progression through pay points Nursing Care
Table 5. Panel logistic regression models of the annual moving propensity of couples between t and t+1
Table 5. Appropriate Legislative Provisions for Further Processing Specify the appropriate legal provision for further processing based on the following: processing is necessary for compliance with a legal obligation to which the controller is subject; (GDPR Art 6. 1 (c)) [delete if not appropriate] processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller (GDPR Art 6. 1 (e)) [delete if not appropriate] [Insert relevant legal ground from 5.1.2 (i) or (ii) here] [Where the legal basis lies in the performance of a legal obligation (GDPR Art. 6(1)(c)) or the exercise of official authority vested in the controller (GDPR Art. 6(1)(e)), include reference to relevant EU or Irish legislative provisions as appropriate] Table 5.1.2
Table 5. Timeline Time Period Goal for number of Site Visits Unique Program Element activities Technical Assistance Target activities
Table 5. 1: Employment outcomes of full-time first degree students (%) 2002/03 03/04 04/05
Table 5. A 8-round linear trail for Friet-PC in the form of masks at the output of ξ in the 8 successive rounds. round δa δb δc weight 0 ...............................1 2 1 ...............................1 ...............................1 ...............................1 2 2 ................8............... ................................ ...............................1 2 3 ................8...8..........1 ................8............... ................8..1............ 6 4 ................4..18...8......1 ....................8..........1 .......1........8...8..........1 10 5 ....4..1........4..14...8...8... ................4..1....8....... ....8...........4..18...8..1...1 14 6 8...c..14.......2...c......18..1 ....4..1............4.......8... ....4..18......14...4.......8. 22 7 8.......c......16...a...8..1...1 8...8...4.......2...8......1...1 8..18..14.......2...c......1...1 22 5.6 Combined Resistance Against 1st Order DPA and SIFA A straightforward Friet-P implementation is vulnerable to SIFA [17] and SIFA- like attacks [28]. A realistic attack scenario would be the following. An adversary has access to the outer part of the state at a given time and can inject a fault during the computation of the permutation in order to recover some information on the inner part of the state. Provided that she can redo the attack multiple times on the same initial state, She could then try to inject a fault in the first round to modify one of the inputs of the AND operation in ξ. A bitflip in an input of a binary AND only propagates to its output if the other input is 1 and hence is only effective in that case. It can hence be simply be derived from the behavior of the fault-detection mechanism. Simulating probabilistic or less precise fault models such as, e.g., the random-AND fault model or a byte-based fault model would also yield exploitable results, although the adversary might need to profile the fault behavior of the device in advance with fault templates [28].
Table 5. 2 shows how different methods of in-situ preservation mitigate against specific threats. For example, geotextiles may be used in different ways: as a layer placed between the sediment and the archaeological objects, or as a barrier method, wrapping objects or a structure. These different uses mean it may be effective in different scenarios. The rubber sheeting method that was used on the Stora Sofia in Sweden represents various methods that cover a site, but which do not actively capture sand. These kinds of methods should be used in combination with, for example, additional sand deposits. 125 See, for example, the devastating effect of the Teredo navalis on the wrecks in the Oostvoornsemeer. xxxx://xxx.xxxxxxxxxxxxxxxxx.xx/magazine/MP03/ MP03_05.1.htm (accessed 30-01-2017). 126 On the Xxxxxxx Xxxxx, the Stirling Castle (wrecked 1703), and in the Southern Delta, the Roompot (wrecked 1853). Both are wrecks that have begun protruding from the seabed due to sand shifts. They belonged to the best preserved sites in northwestern Europe, but have been deteriorating rapidly over the last couple of years. Another wreck that is well preserved, with the boards still standing at least 3 metres is the OVM 14. This wreck, which lies at 30 metres depth in the Oostvoornsemeer, is now under threat of being destroyed by the Teredo navalis, which has been reintroduced into the lake due to the salinization of the water. 127 See Chapter 3. 128 See, for example, the natural conditions around the well-preserved shipwrecks in the Baltic Sea such as the Ghost Wreck or those in the Black Sea. 129 See Chapter 3 on mitigation against multiple threats.
Table 5. Ablation studies of the gradual sparsity increase schedule. The number of training epochs are 3, 5 and 5 for MNLI, QQP and FEVER respectively. The subnetworks are at 90% sparsity. The numbers in the subscripts are standard deviations. gradual soft MNLI HANS QQP PAWSqqp PAWSqqp FEVER Symm1 Symm2 fixed hard 72.090.92 72.630.31 52.560.92 52.820.47 fixed hard 71.641.85 77.080.66 55.701.92 46.483.55 49.591.84 49.380.98 fixed hard 49.565.09 72.800.95 27.452.94 46.670.73 29.754.40 52.330.75 0.2∼0.9 73.610.28 75.060.31 53.900.87 54.991.28 0.2∼0.9 75.790.39 77.540.47 51.570.69 50.920.97 47.940.98 48.860.89 0.2∼0.9 73.531.36 77.01 . 46.471.66 49.87 . 52.421.39 56.57 . 0.5∼0.9 gradual soft 0.5∼0.9 gradual soft 0.5∼0.9 0.7∼0.9 76.840.46 56.720.75 0.7∼0.9 79.490.58 46.591.81 51.150.73 0.7∼0.9 79.010.68 51.740.71 58.170.33 Table 6: Results of XxXXXXx-base and XXXX-large on the NLI task. We conduct mask training with XxX loss on the standard fine-tuned PLMs. “0.5 0.7" denotes gradual sparsity increase. The numbers in the subscripts are standard deviations. XxXXXXx-base MNLI XXXX full model std 87.140.21 68.330.88 xxx 86.560.18 76.151.35 0.5 85.400.14 75.170.55 XXXX-large MNLI HANS full model std 86.840.13 69.442.39 xxx 86.250.17 76.271.55 0.5 85.470.28 75.400.64 mask train 0.7 83.480.29 68.631.33 mask train 0.7 77.546.10 60.197.56 0.5∼0.7 84.410.15 71.951.23 0.5∼0.7 84.830.26 70.182.24
Table 5. Appropriate Legislative Provisions for Further Processing Specify the appropriate legal provision for further processing based on the following: HIQA will process the data, pursuant to the following functions as set out in section 8 (1) (g) of the 2007 Act, that is ‘to operate such other schemes aimed at ensuring safety and quality in the provision of the services as the Authority considers appropriate’. All further processing is equally carried out in the public interest under Article 6 1 (e) of the GDPR. Survey responses will be used in the public interest to identify initiatives that will improve experience across all healthcare settings for end- of-life care.
