RaVÆn: unsupervised change detection of extreme events using ML on-board satellites.

Applications such as disaster management enormously benefit from rapid availability of satellite observations. Traditionally, data analysis is performed on the ground after being transferred-downlinked-to a ground station. Constraints on the downlink capabilities, both in terms of data volume and timing, therefore heavily affect the response delay of any downstream application.

Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization.

Traditional approaches to outdoor vehicle localization assume a reliable, prior map is available, typically built using the same sensor suite as the on-board sensors used during localization. This work makes a different assumption. It assumes that an overhead image of the workspace is available and utilizes that as a map for use for range-based sensor localization by a vehicle.

kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation.

This paper presents a novel two-stage system which integrates topological localisation candidates from a radar-only place recognition system with precise pose estimation using spectral landmark-based techniques. We prove that the-recently available-seminal radar place recognition (RPR) and scan matching sub-systems are complementary in a style reminiscent of the mapping and localisation systems underpinning visual teach-and-repeat (VTR) systems which have been exhibited robustly in the last decade. Offline experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community with performance comparing favourably with and even rivalling alternative state-of-the-art radar localisation systems.

Empowering phase II clinical trials to reduce phase III failures.

The large number of failures in phase III clinical trials, which occur at a rate of approximately 45%, is studied herein relative to possible countermeasures. First, the phenomenon of failures is numerically described. Second, the main reasons for failures are reported, together with some generic improvements suggested in the related literature.

Computing individual and collective ethical utility for optimally planning phase III trials.

A quantitative evaluation of individual and collective ethics is proposed here, with the aim of providing a tool for sample size determination/estimation that goes further than the standard power setting of 80-90%. Individual ethics deal with issues that concern the patients enrolled in the trial, where collective ones concern the patients not enrolled in the trial, and who might benefit from a positive result. The global ethical utility (GEU) of a phase III trial is introduced here, being the summation of individual and collective ethical utilities, and can be viewed as a function of the sample size.

Profit Evaluations When Adaptation by Design Is Applied.

Background: Adaptation by design consists in conservatively estimating the phase III sample size on the basis of phase II data; it is also called conservative sample size estimation (CSSE). The usual assumptions are that the effect size is the same in both phases and that phase II data are not used for phase III confirmatory analysis. CSSE has been introduced to increase the rate of successful trials, and it can be applied in most clinical areas.

Stability criteria for the outcomes of statistical tests to assess drug effectiveness with a single study.

At least two adequate and well-controlled clinical studies are usually required to support effectiveness of a certain treatment. In some circumstances, however, a single study providing strong results may be sufficient. Some statistical stability criteria for assessing whether a single study provides very persuasive results are known.

Conservative sample size estimation in nonparametrics.

Due to the uncertainty of the results of phase II trials, underpowered phase III trials are often planned. In recent literature the conservative approach for sample size estimation was proposed. Some authors, in the parametric framework, make use of the lower bound of the effect size for conservatively estimating the true power, and so the sample sizes.

Adapting by calibration the sample size of a phase III trial on the basis of phase II data.

The problem of estimating the sample size for a phase III trial on the basis of existing phase II data is considered, where data from phase II cannot be combined with those of the new phase III trial. Focus is on the test for comparing the means of two independent samples. A launching criterion is adopted in order to evaluate the relevance of phase II results: phase III is run if the effect size estimate is higher than a threshold of clinical importance.