Impact of the. Radio Evolution towards 5G on the Transport Network We observe that, the transport network evolution to support 5G RAN has to cope with requirements in term of capacity, latency, Hops lengths, but also has to support different transport model, ranging from Backhaul, XHaul up to Fronthaul imposing stringent requirement as capacity and latency. Last but not least, even the transport network topology has to evolve from what is today, based on tree topology and few rings, to something closer to a full connected topology, the meshed network, managed from a central control. Small cells are currently a focus of research leading to new challenges for the backhaul network because of their dense deployment. The forecasted rise in traffic demand of mobile users has to be met with new network architectures. While the trend of reducing the cell area coverage improves the spectrum spatial efficiency by allowing the carrier frequency reusing, at the same time it imposes challenges in cell edge intelligence and distributed cell control in order to avoid degrading the spectrum temporal efficiency due to the small cell high-density area. Two main aspects may be worth to analyse here are: • the C-RAN approach, having an impact on data traffic and latency that has to be supported inside the Transport network • the maximum connection length that has to be supported having impact on transceiver and antenna performances.
Impact of the. Merger on corporate social responsibility The merger will have no impact on the corporate social responsibility policy of CaixaBank.
Impact of the second curation step In order to further evidentiate the behavior of Holistsic IAA, we use it to quantify the impact of the corpus-level curation step. This step was performed per-language after the usual document- level curation step was accomplished. The data was sorted per-label and the master curators looked at the overall coherence of the annotated text-span label pairs, the context of the spans was also pro- vided. This step lead to several corrections and is understood to have boosted the overall coherence of the dataset, and should be reflected with a higher o value for the corpus. In Table 5 we consider the agreement as mea- sured by Holistic IAA after step 1 and 2 of the curation by considering the 4 most active cura- tors: ai and si denote respectively the agreement percentage between annotators and the support at step i. For step 2, the o value is higher, and the average IAA is 1.6 pts higher, while the average intra-annotator agreement (self-agreement) is 3.5 pts higher. This demonstrates that Holistic IAA is able to capture and quantify the positive impact of the corpus-level curation. In Table 6 we illustrate the impact of exclud- ing Loaded Language (MW:LL) and Name Calling (AR:NCL) from the dataset as these labels constitute nearly half of the annotations and are frequently confused with each other by annotators in terms of absolute number (but not in proportion) as shown in Figure 1 and Table 1. We observe that the agree- ment between annotators can be label specific. In Figure 8 we consider the whole curated dataset and measure the o value between pairs of languages. The columns ai report the value after step 1 and 2 considering the whole range of la- bels, while the columns a′i exclude the two labels MW:LL and AR:NCL. Doing so gives us an under- standing of the agreement for the lower populated labels. Please note that all the Attacks on Repu- tation (AR:*) and Manipulative Wordings (MW:*) were excluded from the second step of the curation due to time constraints - except for DE and PL. The impact of the second curation step is almost always positive for all pairs of languages, except notably for one language for which the related o values de- teriorate and which drags down the intra-language coherence score. cur1 cur2 a1 s1 a2 s2 A A 0.597 193778 0.603 177604 A B 0.5 57351 0.517 54503 A C 0.586 45694 0.595 183937 A D 0.544 51327 0.548 123539 B B 0.49 10319 0.523 10210 B C 0.451 8575 0.434 29189 B D 0.61 11688 0.625 27113 C C 0.597 3185...
Impact of the parties’ incentives on the feasibility, private value, and public value of settlement Armed with this understanding of the parties’ incentives, we are ready to explore the circumstances under which settlements are feasible, and then to assess whether feasible settlements will necessarily inure to the benefit of the customer. In what follows, for simplicity, we will focus on potential agreements under which the licensee (alleged infringer) pays a running royalty to the licensor (patentee) and exclude complications such as lump sum payments (in either direction), cross-licenses, and other trappings that may well come into play in real life. The condition under which a given license agreement is feasible is simple enough to state: an agreement is feasible if it offers the litigants—patentee and alleged infringer alike—an alternative that each prefers to continued litigation. We assume, moreover, that a party will prefer the settlement agreement if that party’s total profits under the agreement are no lower than the mathematical expectation (the probability-weighted average) of the value of litigation.17 Let P represent the probability that the patentee prevails in the lawsuit, let PROFITDE represent the entrant’s profit under a pure duopoly (i.e., the situation in which the parties do not settle and the patentee loses the lawsuit), let PROFITDP represent incumbent patentee’s profit under the same scenario, and let PROFITMP represent the patentee’s profit in the event that it wins the lawsuit and retains its patent monopoly. Finally, let R
Impact of the variance bound In this subsection we keep the insured’s initial wealth unchanged and analyze the impact of the variance bound on her demand for insurance. Consider two variance bounds with 0 < ν1 < ν2 < var[X] and denote the corresponding optimal indemnity functions by I1∗ and I2∗ and the parameters by β1∗ and β2∗, respectively. Thus, the insurer’s risk exposures, eIi∗ (x) = Ii∗(x) − E[Ii∗(X)], i = 1, 2, satisfy Ur(w0 − x + eIi∗ (x)) − 2βi∗eIi∗ (x) − E[Ur(w0 − X + eIi∗ (X))] = 0 (4.4) and E[eI∗ (X)] = 0 and E[(eI∗ (X)) ] = var[eI∗ (X)] = νi. (4.5) The following theorem illustrates how the insurer’s risk exposure responds to the change in the variance bound.
