The Future of Virtual Reality and Deep Learning in Visual Field Testing

The visual field (VF) examination is a useful clinical tool for monitoring a variety of ocular diseases. Despite its wide utility in eye clinics, the test as currently conducted is subject to an array of issues that interfere in obtaining accurate results. Visual field exams of patients suffering from additional ocular conditions are often unreliable due to interference between the comorbid diseases. To improve upon these shortcomings, virtual reality (VR) and deep learning are being explored as potential solutions. Virtual reality has been incorporated into novel visual field exams to provide a portable, 3D exam experience. Deep learning, a specialization of machine learning, has been used in conjunction with VR, such as in the iGlaucoma application, to limit subjective bias occurring from patients' eye movements. This chapter seeks to analyze and critique how VR and deep learning can augment the visual field experience by improving accuracy, reducing subjective bias, and ultimately, providing clinicians with a greater capacity to enhance patient outcomes.

Generating fuzzy sets from the families of sets

The main purpose of this paper is to establish a mechanical procedure to determine the membership functions using the data collected from the economic and engineering problems. Determining the membership functions from the collected data may depend on the subjective viewpoint of decision makers. The mechanical procedure proposed in this paper can get rid of the subjective bias of decision makers. The concept of solid families is also proposed by regarding the sets in a family to be continuously varied. The desired fuzzy sets will be generated in the sense that its α-level sets will be identical to the sets of the original family. In order to achieve this purpose, any arbitrary families will be rearranged as the nested families by applying some suitable functions to the original families that are formulated from the collected data.

Reason, Emotion and Solidarity in Humanitarian Advocacy

Evidence-based advocacy is all the rage in humanitarian action. It is premised on rational thinking, which posits that factual evidence can limit subjective bias in humanitarians’ call for change. Data has come to be a cornerstone of this turn towards reason, aggregating human stories in numbers and percentages, which when reaching an elusive threshold is expected to persuade decision-makers to act. This article claims that the prominence of data and facts comes at the cost of understanding people’s concerns and aspirations, and reveals an increasingly emotions-scarce and morally depleted humanitarian enterprise. Examining Médecins Sans Frontières concept of témoignage, the article argues that the pull between reason and emotion crystallises a more profound tension between the need for a professional and technical humanitarianism as opposed to a political and morally charged one. It concludes that the prism of solidarity can help reinvigorate humanitarian advocacy helping reconcile reason with emotion, combining practices of advocacy with those of activism, in turn creating the foundations of a more solidarist humanitarianism.

COUNTEN – an AI-driven tool for rapid, and objective structural analyses of the Enteric Nervous System

Healthy gastrointestinal functions require a healthy Enteric Nervous System (ENS). ENS health is often defined by the presence of normal ENS structure. However, we currently lack a comprehensive understanding of normal ENS structure as current methodologies of manual enumeration of neurons within tissue and ganglia can only parse limited tissue regions; and are prone to error, subjective bias, and peer-to-peer discordance. Thus, there is a need to craft objective methods and robust tools to capture and quantify enteric neurons over a large area of tissue and within multiple ganglia. Here, we report on the development of an AI-driven tool COUNTEN which parses HuC/D-immunolabeled adult murine myenteric ileal plexus tissues to enumerate and classify enteric neurons into ganglia in a rapid, robust, and objective manner. COUNTEN matches trained humans in identifying, enumerating and clustering myenteric neurons into ganglia but takes a fraction of the time, thus allowing for accurate and rapid analyses of a large tissue region. Using COUNTEN, we parsed thousands of myenteric neurons and clustered them in hundreds of myenteric ganglia to compute metrics that help define the normal structure of the adult murine ileal myenteric plexus. We have made COUNTEN freely and openly available to all researchers, to facilitate reproducible, robust, and objective measures of ENS structure across mouse models, experiments, and institutions.

Against Bidimensional Supremacy in EU Constitutionalism

Scholarly consensus sees EU supremacy as “necessarily bidimensional”: the supranational dimension necessarily stands alongside the national dimension, which rejects the absolute and unconditional supremacy of EU law. I argue that this view of bidimensional supremacy is conceptually flawed and descriptively inaccurate. On the conceptual side, I identify the fallacy of symmetry (the idea that national and supranational perspectives on supremacy are similar in nature and equally reductionist), the fallacy of selection (the view that bidimensionalism alone can overcome what it perceives as an inevitable subjective bias in the choice between national and supranational supremacy claims), and the fallacy of construction (an originally shared popular sovereignty theory, which turns out to be riddled with biases that disrupt the equilibrium within the internally divided sovereign). On the interpretative side, I suggest that the empirical evidence in support of bidimensional supremacy is weaker than it is generally assumed. I then offer an interpretation of the PSPP judgment of the German Federal Constitutional Court, which holds a judgment of the Court of Justice of the European Union to be ultra vires, unlawful and thus non-binding. PSPP presents a problem of German origins and cast, rather than one stemming from the inner structure of EU constitutionalism. At most, PSPP represents a contingent, rather than necessary, and thus unexceptional instance of bidimensional supremacy.

Do different geologists see the same fractures? Quantifying subjective bias in fracture data collection.

<p>The characterisation of natural fracture networks using outcrop analogues is important in understanding subsurface fluid flow and rock mass characteristics in fractured lithologies. It is well known from decision sciences that subjective bias can significantly impact the way data is gathered and interpreted, introducing scientific uncertainty.</p><p>This study investigates the scale of and nature of subjective bias on fracture data collected by geoscientists using four commonly used approaches (linear scanlines, circular scanlines, topology sampling and window sampling) both in the field and in workshops using field photographs.</p><p>We observe considerable variability between each participant’s interpretation of the same scanline, and this variability is seen regardless of participants’ level of geological experience. Geologists appear to be either focussing on the detail or focussing on gathering larger volumes of data; personal character traits that affect the recorded fracture network attributes. As a result, the fracture statistics that are derived from field data can vary considerably for the same scanline, depending on which geologist collected the data. Additionally, the personal bias of geologists collecting the data affects the scanline size (minimum length of linear scanlines, radius of circular scanlines or area of a window sample) needed to collect a statistically representative amount of data.</p><p>Based on our findings and on understanding of bias reduction in decision sciences, we suggest protocols to recognise, understand and limit the effect of subjective bias on fracture data biases during data collection.</p><p>Our work shows the capacity for cognitive biases to introduce uncertainty into observation-based data. Fracture statistics derived from field data often inputs into geological models that are used for a range of applications, from understanding fluid flow to characterising rock strength, and so these uncertainties have ramifications for propagation into a range of outcomes. Importantly, our findings that personal bias can affect data collection have implications well beyond the geosciences.</p>

The European Commission’s Google Shopping decision: Could bias have anything to do with it?

Google has been on the radar of the European Commission’s Directorate-General for Competition for some time. Over the last few years, the European Commission has launched competition law investigations into three Google services: Google Shopping, Android and AdSense. In June 2017, the Commission released its decision in the Google Shopping case. The Commission imposed a record fine of €2.42 billion on Google for violating EU antitrust rules. According to the Commission, Google abused its market dominance as a search engine by giving an ‘illegal advantage’ to its own advertisers through its comparison shopping service. The Google Shopping decision can be understood to a significant degree by reference to conscious and unconscious biases. These biases, of course, are not overt – in administrative decision-making, decision-makers have to apply the law and support their decisions with reasons. Legal reasoning, however, provides an opportunity to test the plausibility of hypothesized bias: if the reasoning is strong, persuasive and objective, bias is either irrelevant (that is, it has not influenced the decision) or unlikely. If reasoning is weak, unpersuasive, or subjective, bias may have played a role. As this article demonstrates, based on careful analysis of the Commission’s reasoning in the Google Shopping case, the hypothesis of possible bias is confirmed.

How do we see fractures? Quantifying subjective bias in fracture data collection

The characterisation of natural fracture networks using outcrop analogues is important in understanding subsurface fluid flow and rock mass characteristics in fractured lithologies. It is well known from decision sciences that subjective bias can significantly impact the way data are gathered and interpreted, introducing scientific uncertainty. This study investigates the scale and nature of subjective bias on fracture data collected using four commonly applied approaches (linear scanlines, circular scanlines, topology sampling, and window sampling) both in the field and in workshops using field photographs. We demonstrate that geologists' own subjective biases influence the data they collect, and, as a result, different participants collect different fracture data from the same scanline or sample area. As a result, the fracture statistics that are derived from field data can vary considerably for the same scanline, depending on which geologist collected the data. Additionally, the personal bias of geologists collecting the data affects the scanline size (minimum length of linear scanlines, radius of circular scanlines, or area of a window sample) needed to collect a statistically representative amount of data. Fracture statistics derived from field data are often input into geological models that are used for a range of applications, from understanding fluid flow to characterising rock strength. We suggest protocols to recognise, understand, and limit the effect of subjective bias on fracture data biases during data collection. Our work shows the capacity for cognitive biases to introduce uncertainty into observation-based data and has implications well beyond the geosciences.