Academic literature on the topic 'Beyond worst-case analysis'

Over the past decade, interval arithmetic (IA) has been used to determine tolerance bounds of phased-array beampatterns. IA only requires that the errors of the array elements are bounded and can provide reliable beampattern bounds even when a statistical model is missing. However, previous research has not explored the use of IA to find the error realizations responsible for achieving specific bounds. In this study, the capabilities of IA are extended by introducing the concept of “backtracking,” which provides a direct way of addressing how specific bounds can be attained. Backtracking allows for the recovery of the specific error realization and corresponding beampattern, enabling the study and verification of which errors result in the worst-case array performance in terms of the peak sidelobe level (PSLL). Moreover, IA is made applicable to a wider range of arrays by adding support for arbitrary array geometries with directive elements and mutual coupling in addition to element amplitude, phase, and positioning errors. Last, a simple formula for approximate bounds of uniformly bounded errors is derived and numerically verified. This formula gives insights into how array size and apodization cannot reduce the worst-case PSLL beyond a certain limit.

261 Background: Recent clinical trials of novel systemic therapies showed improved survival of patients with metastatic esophageal cancer (EC) and gastric cancer (GC). Survival improvements observed in clinical trials might be unrepresentative for the total population as the percentage of patients whom participate in clinical trials is limited and more than half of all patients receive best supportive care (BSC). The aim of our study is to assess the best-case, typical and worst-case survival scenarios in patients with metastatic esophagogastric cancer. Methods: We selected patients with metastatic EC (including junction) or GC diagnosed in 2006-2019 from the Netherlands Cancer Registry. Survival scenarios were calculated based on percentiles of the survival curve stratified by tumor location and treatment (tumor-directed therapy or BSC). Survival scenarios were calculated for the 10th (best-case), 25th (upper-typical), 75th (lower-typical) and 90th (worst-case) percentiles. Linear trend analysis was performed to test if changes in survival over the diagnosis years were significant. Results: We identified 12739 patients with EC and 6833 patients with GC. Percentage of patients receiving tumor-directed therapy increased from 34% to 47% and 30% to 45% for patients with EC and GC, respectively. The median survival remained unchanged for patients with EC (5.0 months) and improved slightly for patients with GC (3.1 to 3.7 months; P=0.006). For patients with EC survival of the best-case scenario improved (17.4 to 22.8 months; P=0.001), whereas the other scenarios remained unchanged: upper-typical 11.2 to 11.7 (P=0.11), lower-typical 2.1 to 2.0 (P=0.10) and worst-case 0.9 to 0.8 months (P=0.22). For patients with GC survival improved for the best-case (13.1 to 19.5; P=0.005) and upper-typical scenario (6.7 to 10.6 months; P=0.002), whereas the lower-typical (1.2 to 1.4 months; P=0.87) and worst-case (0.6 to 0.6 months; P=0.60) remained unchanged. For patients with EC receiving tumor-directed therapy survival in all scenarios remained unchanged while for patients receiving BSC survival decreased: best-case 11.8 to 9.8 (P=0.005), upper-typical 6.0 to 5.0 (P=0.002), lower-typical 1.4 to 1.0 (P=0.003) and worst-case 0.7 to 0.5 months (P=0.03). For patients with GC receiving tumor-directed therapy survival improved for all scenarios: best-case 19.8 to 30.4 (P=0.005), upper-typical 6.4 to 10.3 (P=0.002), lower-typical 3.6 to 5.4 (P<0.001) and worst-case 1.4 to 2.6 months (P<0.001), and for patients receiving BSC survival for all scenarios remained unchanged. Conclusions: The proportion of patients with EC and GC receiving tumor-directed therapy increased over time. Despite the fact that survival improvements were not observed across all scenarios, at least an increase in survival was observed in certain subgroups of patients.

Traditional tolerance analyses such as the worst case methods and the statistical methods are applicable to rigid body assemblies. However, for flexible sheet metal assemblies, the traditional methods are not adequate: the components can deform, changing the dimensions during assembly. This paper evaluates the effects of deformation on component tolerances using linear mechanics. Two basic configurations, assembly in series and assembly in parallel, are investigated using analytical methods. Assembly sequences and multiple joints beyond the basic configurations are further examined using numerical methods (with finite element analysis). These findings constitute a new methodology for the tolerancing of deformable parts.

The objectives of this study were: (1) to explore cultural transition pathways of Swedish professional football players relocated to another European country, (2) to identify shared themes in their transition narratives. We interviewed three professional players who in their early twenties relocated to Italy, Turkey, and Switzerland, and then analyzed their stories using holistic and categorical analyses following the narrative oriented inquiry (NOI) model (Hiles & Čermák, 2008). The holistic analysis resulted in creating three core narratives (i.e., re-telling of the participants’ stories) entitled: Preparing for the worst-case scenario and saved by dedication to football; Showing interest for the host culture and carrying responsibility as a foreign player; and A step for personal development: from homesickness to being hungry for more. The categorical analysis resulted in 12 shared themes from the players’ stories arranged around three phases of the cultural transition model (Ryba et al., 2016). In the pre-transition all the participants were established players searching for new professional opportunities. In the acute cultural adaptation phase, they all prioritized adjustment in football (e.g., fitting in the team, performing). In the socio-cultural adaptation phase, they broaden their perspectives and realized that finding a meaningful life outside of football was just as important to function and feel satisfied as football success.

This paper considers the recently popular beyond-worst-case algorithm analysis model which integrates machine-learned predictions with online algorithm design. We consider the online Steiner tree problem in this model for both directed and undirected graphs. Steiner tree is known to have strong lower bounds in the online setting and any algorithm’s worst-case guarantee is far from desirable. This paper considers algorithms that predict which terminal arrives online. The predictions may be incorrect and the algorithms’ performance is parameterized by the number of incorrectly predicted terminals. These guarantees ensure that algorithms break through the online lower bounds with good predictions and the competitive ratio gracefully degrades as the prediction error grows. We then observe that the theory is predictive of what will occur empirically. We show on graphs where terminals are drawn from a distribution, the new online algorithms have strong performance even with modestly correct predictions.

When a computer system schedules jobs there is typically a significant cost associated with preempting a job during execution. This cost can be incurred from the expensive task of saving the memory’s state or from loading data into and out of memory. Thus, it is desirable to schedule jobs non-preemptively to avoid the costs of preemption. There is a need for non-preemptive system schedulers for desktops, servers, and data centers. Despite this need, there is a gap between theory and practice. Indeed, few non-preemptive online schedulers are known to have strong theoretical guarantees. This gap is likely due to strong lower bounds on any online algorithm for popular objectives. Indeed, typical worst-case analysis approaches, and even resource-augmented approaches such as speed augmentation, result in all algorithms having poor performance guarantees. This article considers online non-preemptive scheduling problems in the worst-case rejection model where the algorithm is allowed to reject a small fraction of jobs. By rejecting only a few jobs, this article shows that the strong lower bounds can be circumvented. This approach can be used to discover algorithmic scheduling policies with desirable worst-case guarantees. Specifically, the article presents algorithms for the following three objectives: minimizing the total flow-time, minimizing the total weighted flow-time plus energy where energy is a convex function, and minimizing the total energy under the deadline constraints. The algorithms for the first two problems have a small constant competitive ratio while rejecting only a constant fraction of jobs. For the last problem, we present a constant competitive ratio without rejection. Beyond specific results, the article asserts that alternative models beyond speed augmentation should be explored to aid in the discovery of good schedulers in the face of the requirement of being online and non-preemptive.