Relative Worst-Order Analysis: A Survey

Relative worst-order analysis is a technique for assessing the relative quality of online algorithms. We survey the most important results obtained with this technique and compare it with other quality measures.Comment: 20 page

GRO Report - August 2015

Relative worst order analysis is a supplement or alternative to competitive analysis which has been shown to give results more in accordance with observed behavior of online algorithms for a range of different online problems. The contribution of this paper is twofold. First, it adds the static list accessing problem to the collection of online problems where relative worst order analysis gives better results. Second, and maybe more interesting, it adds the non-trivial supplementary proof technique of list factoring to the theoretical toolbox for relative worst order analysis

The itinerant list update problem

We introduce the itinerant list update problem (ILU), which is a relaxation of the classic list update problem in which the pointer no longer has to return to a home location after each request. The motivation to introduce ILU arises from the fact that it naturally models the problem of track memory management in Domain Wall Memory. Both online and offline versions of ILU arise, depending on specifics of this application. First, we show that ILU is essentially equivalent to a dynamic variation of the classical minimum linear arrangement problem (MLA), which we call DMLA. Both ILU and DMLA are very natural, but do not appear to have been studied before. In this work, we focus on the offline ILU and DMLA problems. We then give an O(log2n) -approximation algorithm for these problems. While the approach is based on well-known divide-and-conquer approaches for the standard MLA problem, the dynamic nature of these problems introduces substantial new difficulties. We also show an Ω(logn) lower bound on the competitive ratio for any randomized online algorithm for ILU. This shows that online ILU is harder than online LU, for which O(1)-competitive algorithms, like Move-To-Front, are known

Genetic identification of avian samples recovered from solar energy installations.

Renewable energy production and development will drastically affect how we meet global energy demands, while simultaneously reducing the impact of climate change. Although the possible effects of renewable energy production (mainly from solar- and wind-energy facilities) on wildlife have been explored, knowledge gaps still exist, and collecting data from wildlife remains (when negative interactions occur) at energy installations can act as a first step regarding the study of species and communities interacting with facilities. In the case of avian species, samples can be collected relatively easily (as compared to other sampling methods), but may only be able to be identified when morphological characteristics are diagnostic for a species. Therefore, many samples that appear as partial remains, or "feather spots"-known to be of avian origin but not readily assignable to species via morphology-may remain unidentified, reducing the efficiency of sample collection and the accuracy of patterns observed. To obtain data from these samples and ensure their identification and inclusion in subsequent analyses, we applied, for the first time, a DNA barcoding approach that uses mitochondrial genetic data to identify unknown avian samples collected at solar facilities to species. We also verified and compared identifications obtained by our genetic method to traditional morphological identifications using a blind test, and discuss discrepancies observed. Our results suggest that this genetic tool can be used to verify, correct, and supplement identifications made in the field and can produce data that allow accurate comparisons of avian interactions across facilities, locations, or technology types. We recommend implementing this genetic approach to ensure that unknown samples collected are efficiently identified and contribute to a better understanding of wildlife impacts at renewable energy projects

Diagnostic value of MRI in the presurgical evaluation of patients with epilepsy: influence of field strength and sequence selection: a systematic review and meta‐analysis from the E‐PILEPSY Consortium

MRI is a cornerstone in presurgical evaluation of epilepsy. Despite guidelines, clinical practice varies. In light of the E-PILEPSY pilot reference network, we conducted a systematic review and meta-analysis on the diagnostic value of MRI in the presurgical evaluation of epilepsy patients. We included original research articles on diagnostic value of higher MRI field strength and guideline-recommended and additional MRI sequences in detecting an epileptogenic lesion in adult or paediatric epilepsy surgery candidates. Lesion detection rate was used as a metric in meta-analysis. Eighteen studies were included for MRI field strength and 25 for MRI sequences, none were free from bias. In patients with normal MRI at lower-field strength, 3T improved lesion detection rate by 18% and 7T by 23%. Field strengths higher than 1.5T did not have higher lesion detection rates in patients with hippocampal sclerosis (HS). The lesion detection rate of epilepsy-specific MRI protocols was 83% for temporal lobe epilepsy (TLE) patients. Dedicated MRI protocols and evaluation by an experienced epilepsy neuroradiologist increased lesion detection. For HS, 3DT1, T2, and FLAIR each had a lesion detection rate at around 90%. Apparent diffusion coefficient indices had a lateralizing value of 33% for TLE. DTI fractional anisotropy and mean diffusivity had a localizing value of 8% and 34%. A dedicated MRI protocol and expert evaluation benefits lesion detection rate in epilepsy surgery candidates. If patients remain MRI negative, imaging at higher-field strength may reveal lesions. In HS, apparent diffusion coefficient indices may aid lateralization and localization more than increasing field strength. DTI can add further diagnostic information. For other additional sequences, the quality and number of studies is insufficient to draw solid conclusions. Our findings may be used as evidence base for developing new high-quality MRI studies and clinical guidelines