Depiction Of Uncertainty Research Articles

Segmentation of Medical Images with Adaptable Multifunctional Discretization Bayesian Neural Networks and Gaussian Operation

Bayesian statistics is incorporated into a neural network to create a Bayesian neural network (BNN) that adds posterior inference aims at preventing overfitting. BNNs are frequently used in medical image segmentation because they provide a stochastic viewpoint of segmentation approaches by producing a posterior probability with conventional limitations and allowing the depiction of uncertainty over following distributions. However, the actual efficacy of BNNs is constrained by the difficulty in selecting expressive discretization and accepting suitable following disseminations in a higher-order domain. Functional discretization BNN using Gaussian processes (GPs) that analyze medical image segmentation is proposed in this paper. Here, a discretization inference has been assumed in the functional domain by considering the former and dynamic consequent distributions to be GPs. An upsampling operator that utilizes a content-based feature extraction has been proposed. This is an adaptive method for extracting features after feature mapping is used in conjunction with the functional evidence lower bound and weights. This results in a loss-aware segmentation network that achieves an F1-score of 91.54%, accuracy of 90.24%, specificity of 88.54%, and precision of 80.24%.

Uncertainty quantification and propagation with probability boxes

In the last decade, the best estimate plus uncertainty methodologies in nuclear technology and nuclear power plant design have become a trending topic in the nuclear field. Since BEPU was allowed for licensing purposes by the most important regulator bodies, different uncertainty assessment methods have become popular, overall non-parametric methods. While non-parametric tolerance regions can be well stated and used in uncertainty quantification for licensing purposes, the propagation of the uncertainty through different codes (multi-scale, multiphysics) in cascade needs a better depiction of uncertainty than the one provided by the tolerance regions or a probability distribution. An alternative method based on the parametric or distributional probability boxes is used to perform uncertainty quantification and propagation regarding statistic uncertainty from one code to another. This method is sample-size independent and allows well-defined tolerance intervals for uncertainty quantification, manageable for uncertainty propagation. This work characterizes the distributional p-boxes behavior on uncertainty quantification and uncertainty propagation through nested random sampling.

How are map-based decisions influenced by uncertainty visualization in risky and time-critical situations?

How are map-based decisions influenced by uncertainty visualization in risky and time-critical situations?

Depiction of uncertainty in the visually interpreted land cover data

Remote sensing data analysis to infer land cover and the subsequent modelling of land use change are subject to uncertainties, which may have an impact on the accuracy of future land-use predictions. Part of these uncertainties come from the visual interpretation of remote sensing data as during this phase a specific knowledge and expertise are crucial. Visual interpretation includes the meaning of the image content but also goes beyond what can be seen on the image to recognize spatial and landscape patterns. The quality of recognition depends on the expertise in image interpretation and visual perception. Based on this statement, it is necessary to visualize also the information about uncertainty. This paper describes different perspectives of uncertainty visualization on the example of visually interpreted aerial photographs in a mining area. The study is focused on visualization techniques for uncertainty awareness analysis of land cover data. This approach should contribute to better understanding, assessment and potential spreading of visual information of uncertainty and help to appraise the data critically. The goal of this paper is to raise uncertainty awareness among practitioners who deal with land cover data and stress the visualizations techniques as a more reliable means to assess the quality, and hence the uncertainty, of these data.

David Lynch Swerves: Uncertainty from Lost Highway to Inland Empire by Martha P. Nochimson

Reviewed by: David Lynch Swerves: Uncertainty from Lost Highway to Inland Empire by Martha P. Nochimson Justus Nieland (bio) David Lynch Swerves: Uncertainty from Lost Highway to Inland Empire by Martha P. Nochimson. University of Texas Press. 2013. $36.85 hardcover; $16.75 paper. 295 pages. Does David Lynch really just want us to be happy? Should his cinema be considered a suite of filmic fables that seek to set spectators on the path to bliss and enlightenment? Martha P. Nochimson thinks so and seeks to fuse the domains of quantum physics and mysticism in David Lynch Swerves, a companion volume to her 1997 study of the director, The Passion of David Lynch: Wild at Heart in Hollywood.1 Nochimson’s latest work offers close readings of the four feature films Lynch has completed over the past two decades—Lost Highway (1997), The Straight Story (1999), Mulholland Drive (2001), and Inland Empire (2006)—which she considers a qualitatively discrete group in Lynch’s oeuvre. For her, the quartet constitutes Lynch’s “second-stage cinema” largely because the films are “increasingly audacious in their depiction of uncertainty,” an encounter with the strangeness of matter itself that Nochimson attributes to Lynch’s “impressionistic gleaning of physics.”2 For readers who may be unfamiliar with The Passion of David Lynch, I should note that Nochimson’s method in that earlier work was to read Lynch’s films, from Eraserhead (1977) to Twin Peaks: Fire Walk with Me (1992), as allegories of their protagonists’ visionary capacities—their receptivity to energies and states of consciousness that exist “beyond the entrenched ordinary limitations of cultural and linguistic and social structures.”3 Nochimson’s pursuit of the Lynchian beyond was Jungian, partially, and in a fashion that other critics of Lynch’s work—most notably Slavoj Žižek—cast as basically New Age in orientation.4 [End Page 165] Lynch’s “second-stage” films, Nochimson now posits, are marked by protagonists with a “dubious trajectory; Lynch builds suspense about whether or not they will interpret their experience of energies from beyond in a negative or positive fashion.”5 From Lost Highway’s Fred Madison (Bill Pullman) to Inland Empire’s Nikki Grace (Laura Dern), these second-stage seekers are increasingly beset by the vexations of matter in a quantum universe. Such is the book’s subtitular “uncertainty,” which pervades the reality of the so-called marketplace of quotidian striving that the Lynchian quester must negotiate and transcend. When Lynch’s characters conquer the fear that accompanies the “shattering of our illusions about matter,” they affirm Lynch’s belief that “an enduring security is possible for humanity,” and are bathed—often literally so—in the “light of [an] optimistic belief” in a “universe of immense power and beauty beyond the limits of human cultures.”6 What is admirable in this approach is its initial proposal to rethink Lynch as a materialist. A “full and rich reading of Lynch,” Nochimson insists, must “not only take into account the many levels of human interiority, but also Lynch’s images that reference the many possibilities offered by the multiple levels of the external world of matter.”7 Here, and in her readings of the individual films, Nochimson attempts to mobilize concepts and terms from the field of quantum mechanics (“superposition,” “entanglement,” “nonlocality,” “decoherence”). This discipline’s attentiveness to the essential instability of things and the foundational strangeness of the universe would seem to comport with the multiple affronts to causality, logic, and the Newtonian behavior of bodies and objects apparent to even the most casual Lynchian fan, who knows well that shit is far out in the “Lynchverse.” One might call it “a strange world”—“wild at heart and weird on top,” you could even say. But Nochimson, ultimately, is less interested in quantum mechanics itself—or the materialism underlying it—than she is in brokering a compromise between modern physics and a Lynchian cosmology, which requires drawing analogies between the “essential limitlessness” of the building blocks of matter on a particle level and Lynch’s faith in a “paradigm of cosmic ‘beyondness’” that “comes from the Vedic tradition.”8 This qualification is crucial, as it fuels Nochimson’s way of...

Risk Characterization and the Red Book

Of the four components of the risk paradigm, Risk Characterization receives the briefest description in die National Research Council's report Risk Assessment in the Federal Government: Managing the Process, also called the “Red Book.” It is often stated that two decades ago when the Red Book was written there was the naive belief that Risk Assessment could be completely separated from Risk Management. However, it was clear at the time that there was an overlap between the two, occurring at the Risk Characterization step. The expansion in knowledge about the understanding of risk has led to a heightened interest in risk characterization and communication. Unfortunately, in the rush to use new tools, there has been an overstatement of the value of the routine mathematical depiction of uncertainty, rather than the written description called for in the Red Book.