Robustness. The probability that the following experiment outputs “Eve wins” is at most δ: sample (w, wj, e) from (W, W j, E); let ca, cb be the communi- cation upon execution of (A, B) with Xxx(e) actively controlling the chan- nel, and let A(w, ca, ra) = kA, B(wj, cb, rb) = kB. Output “Xxx wins” if (kA ƒ= kB ∧ kA ƒ=⊥ ∧kB ⊥).
Robustness. In view of the uncertainties, are the model results robust enough for policy advice or are there alternative ways conceivable for attaining more robust conclusions?
Robustness. Regarding
Robustness. Robustness has two distinct dimensions, strong and fragile tenacity. The resilience of the social choice mechanism addresses regime’s capacity to adapt to changes or disturbances that occur in the wider social environment without radical transformation (Young, 1992: 179). Japan and Indonesia implement a strong institutional system. It lies in provision 10 of the MoU which points out clearly that both parties facilitate each other through financial, technology and capacity building supports necessary for JCM implementation. Article 2 of the UNFCCC as the objective of JCM program implementation becomes a milestone of cooperation relation between the two countries that is not easily vulnerable. Not to mention, two parties conduct close policy consultation at various levels as mentioned in provision 2. Rules in the MoU agreed by Japan and Indonesia are dynamic, meaning it is very possible if there is an amendment or change of rules in accordance with the terms and agreement of both parties. Moreover, this is supported by form of soft legalization both parties apply with the result that it allows for changes in the rules if required in the future in a line with provisions 13 & 14 in the MoU.
Robustness. The interface between Secure Media Recording Client Software and DVD Players, and the interface between Secure Media Recording Client Software and Integrated Products, shall meet the CSS Procedural Specifications robustness requirements for software and hardware, in accordance with Sections 6.2.4 and 6.2.5 herein.
Robustness. We developed GAM with the goal of being able to handle graphs with “in- correct” edges (i.e. those that connect nodes with differing labels). We consider such edges “incorrect" under the label propagation assump- tion, despite the fact that they may refer to real- world connections between these nodes (e.g., citations between research articles on different topics). In Xxxx, Citeseer, and Pubmed, 19%, 26%, and 20% of the edges, respectively, are in- correct. To demonstrate the ability of GAM to MLP128 MLP128 + NGM MLP128 + GAM Accuracy (%) 20 30 40 50 60 70 74 handle these incorrect edges and perhaps even higher levels of noise, we performed a robust- ness analysis by introducing spurious edges to the graph, and testing whether our agreement model learns to ignore them. We added spuri- Figure 4: Robustness to noisy graphs. The x axis represents the percentage of correct edges remain- ing after adding wrong edges to the Citeseer dataset. ous edges by randomly sampling pairs of nodes with different true labels until the percentage of incorrect edges met a desired target. We tested the performance of GAM on a set of graphs created in this manner. MLPs are good base model candidates for testing this because they can only be affected by the graph quality through the GAM regularization terms (unlike GCN or GAT, where the graph is implicitly used in the model). The results are shown in Figure 4 on the Citeseer dataset (the hardest of the three datasets), for graphs containing between 5% and 74% correct edges. A plain MLP with 128 hidden units obtains 52.2% accuracy independent of the level of noise in the graph. Adding GAM to this MLP increases its accuracy by about 19%. This improvement persists even as the fraction of correct edges decreases. For example, the accuracy remains 70% even in the case where only 5% of the graph edges are correct. In contrast, the performance of NGM steadily decreases as the fraction of incorrect edges increases, to the point where it starts performing worse than the plain MLP (when the percent of correct edges ≤ 60%), and it is thus preferable not to use it.
Robustness. Informally, a scheme is robust if no adversary can prevent sufficiently many honest parties from generating an accepting signature on a message. We define robustness as a game between a challenger and an adversary . The game is formally defined in Figure 2 and comprises of three phases. In the setup and corruption phase, the challenger generates the public parameters pp and a pair of signature keys for every party. Given pp and all verification keys vk1, . . . , vkn, the adversary can adaptively corrupt a subset of t parties and learn their secret keys. In the case of a bulletin-board PKI (but not of trusted PKI), the adversary can replace the verification key of the corrupted party by another key of its choice. Unless specified otherwise, we consider the bulletin-board PKI to be the default setup model. mode,Π,A Experiment Exptrobust (κ, n, t) A The experiment Exptrobust is a game between a challenger and the adversary . The game is parametrized by an SRDS scheme Π and proceeds as follows: A
Robustness. Optional redundancy must be possible for every element of the system. The adopted technologies must fulfil this requirement implicitly. The chosen database software and the server that will receive all the queries and commands must support redundancy. Budget of real use case adopters will decide whether to use redundancy or not, but the system must be designed to have the chance to do it. The system functionality must not be affected by corrupted data input (invalid video or audio files, for example). This kind of input must be rejected if there isn’t any profitable data in them but the system availability cannot be compromised.
Robustness. Our results could be sensitive to the pre-disaster periods. We conduct a sensitivity analysis by extending the pre-disaster period to 12 quarters. Tables 7a to 9b show the sensitivity analysis for Thailand’s disasters, while Tables 10a and 10b show those results for the Philippine typhoons. As can be seen, the results are similar to those of the baseline. In the case of Thailand, we generally find a decline in total consumption. This decline stems from a reduction in expenditures on the service sector including transportation, hotels, and restaurants. In contrast, we generally observe increased household spending on food and non- alcoholic drinks, alcoholic beverages and tobacco products, clothing, and utilities. As seen from Table 7a, the total immediate expenditures declined by approximately 26 billion Thai baht after the Indian Ocean tsunami. Similar to our baseline results, we find housing- related expenses, including utilities and furniture, increased during this disaster. However, the estimates of the immediate expenditure declines in recreation, restaurants, and hotels are imprecise (Table 7b); yet we find the expenditure on transportation immediately dropped. Table 8a shows total consumption expenditure immediately dropped by approximately 70 billion Thai baht due to the 2011 Thailand floods. The results presented in Tables 8a and 8b resemble those of the baseline. Specifically, we find households immediately increased spending on both durable and non-durable goods. On the other hand, we find consumers immediately reduced their spending on transportation, restaurants, and hotels. Tables 9a and 9b show the results pertaining to the 2016-17 Thailand floods. We again find similar results to the baseline estimates. The total immediate consumption dropped approximately 31 billion Thai baht. Similar to aforementioned disasters in Thailand, households immediately increased spending on non-durable goods including food, beverages, tobacco, and clothing. Households also increased immediate spending on utilities. However, they reduced their spending on transportation, restaurants, and hotels. For the Philippines, the effects of the typhoons on consumption are usually small. Among the three typhoons, we still find that Typhoon Haiyan had the largest immediate effects on consumption expenditures; the total household spending immediately declined by approximately 40 billion pesos after Typhoon Haiyan. Although the magnitude of estimate shown in Table 10a is simi...
Robustness. Analyst use the term “robustness” of an estimator in at least two different senses. One is whether an estimator is robust to violations of the distributional assumptions that might have justified the estimator. For instance, if the ML estimator is based on assuming that the observed variables come from a multinormal distribution, then do the same properties hold if they come from nonnormal distributions. A second sense of robustness concerns whether the properties of the estimator hold when there are structural misspecifications such as omitting variables, omitting paths, or failing to include correlated errors. Author Manuscript The asymptotic properties of the MIIV-2SLS estimator that I described in an earlier section do not depend on the observed variables coming from a normal distribution (see Bollen, 1996). In this sense, the MIIV-2SLS estimator is a “distribution-free” estimator. In addition, it is easy to bootstrap the MIIV-2SLS estimator to develop an alternative estimate of its standard errors for coefficients using MIIVsem (Xxxxxx et al., 2017). However, of more interest and more neglected is the robustness of an estimator to structural misspecifications. The literature on robustness to structural misspecification is sparse and those papers that xxxxxx the topic focus on the ML system wide estimator (e.g., Xxxxxx, 1989; Xxxxxx & Xxxxxx, 1993; Xxxx, Xxxxxxxx, & Xxxxxxx, 2003; Xxxx, Xxxxxx, & Xxxxxx, 2008). Author Manuscript A general analytic result on when the MIIV-2SLS is robust to structural misspecifications is in Bollen (2001, p. 130): “Suppose that for the j-th equation in the correctly specified model, the model-implied IVs are in a matrix Vj. The 2SLS estimator of the coefficients in Aj is robust for any misspecifications in other equations under two conditions: (1) the equation being estimated is correctly specified, and (2) the misspecifications in the other equations do not alter the variables in Vj.” In essence, this says that the MIIV-2SLS estimator for an equation is robust when the equation is correctly specified and the structural misspecifications in the other equations do not change the MIIVs for that equation. Author Manuscript A few examples can illustrate the power of these robustness conditions and the benefit of having them. To start with I return to the industrialization and political democracy example as shown in Figure 4. Assume that the correct structural equation model includes both the solid and the dashed lines shown ...
