This paper introduces progressive algorithms for the topological analysis of scalar data. Our approach is based on a hierarchical representation of the input data and the fast identification of topologically invariant vertices, for which we show that no computation is required as they are introduced in the hierarchy. This enables the definition of efficient coarse-to-fine topological algorithms, which leverage fast update mechanisms for ordinary vertices and avoid computation for the topologically invariant ones. We instantiate our approach with two examples of topological algorithms (critical point extraction and persistence diagram computation), which generate exploitable outputs upon interruption requests and which progressively refine them otherwise. Experiments on real-life datasets illustrate that our progressive strategy, in addition to the continuous visual feedback it provides, even improves run time performances with regard to non-progressive algorithms and we describe further accelerations with shared-memory parallelism. We illustrate the utility of our approach in (i) batch-mode and (ii) interactive setups, where it respectively enables (i) the control of the execution time of complete topological pipelines as well as (ii) previews of the topological features found in a dataset, with progressive updates delivered within interactive times.
This software paper gives an overview of the features supported by the Topology ToolKit (TTK), which is an open-source library for topological data analysis (TDA). TTK implements, in a generic and efficient way, a substantial collection of reference algorithms in TDA. Since its initial public release in 2017, both its user and developer bases have grown, resulting in a significant increase in the number of supported features. In contrast to the original paper introducing TTK [40] (which detailed the core algorithms and data structures of TTK), the purpose of this software paper is to describe the list of features currently supported by TTK, ranging from image segmentation tools to advanced topological analysis of high-dimensional data, with concrete usage examples available on the TTK website [42].
Background Burnout results from excessive demands at work. Caregivers suffering from burnout show a state of emotional exhaustion, leading them to distance themselves from their patients and to become less efficient in their work. While some studies have shown a negative impact of burnout on physicians’ clinical reasoning, others have failed to demonstrate any such impacts. To better understand the link between clinical reasoning and burnout, we carried out a study looking for an association between burnout and clinical reasoning in a population of general practice residents. Methods We conducted a cross-sectional observational study among residents in general practice in 2017 and 2019. Clinical reasoning performance was assessed using a script concordance test (SCT). The Maslach Burnout Inventory for Human Services Survey (MBI-HSS) was used to determine burnout status in both original standards of Maslach’s burnout inventory manual (conventional approach) and when individuals reported high emotional exhaustion in combination with high depersonalization or low personal accomplishment compared to a norm group (“emotional exhaustion +1” approach). Results One hundred ninety-nine residents were included. The participants’ mean SCT score was 76.44% (95% CI: 75.77–77.10). In the conventional approach, 126 residents (63.31%) had no burnout, 37 (18.59%) had mild burnout, 23 (11.56%) had moderate burnout, and 13 (6.53%) had severe burnout. In the “exhaustion + 1“ approach, 38 residents had a burnout status (19.10%). We found no significant correlation between burnout status and SCT scores either for conventional or “exhaustion + 1“ approaches. Conclusions Our data seem to indicate that burnout status has no significant impact on clinical reasoning. However, one speculation is that SCT mostly examines the clinical reasoning process’s analytical dimension, whereas emotions are conventionally associated with the intuitive dimension. We think future research might aim to explore the impact of burnout on intuitive clinical reasoning processes.
This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.
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