Disparate Domains Research Articles

Functional specificity of attentional modulation in the disparity domain

Functional specificity of attentional modulation in the disparity domain

On the Direction of Time: From Reichenbach to Prigogine and Penrose

The question why natural processes tend to flow along a preferred direction has always been considered from within the perspective of the Second Law of Thermodynamics, especially its statistical formulation due to Maxwell and Boltzmann. In this article, we re-examine the subject from the perspective of a new historico-philosophical formulation based on the careful use of selected theoretical elements taken from three key modern thinkers: Hans Reichenbach, Ilya Prigogine, and Roger Penrose, who are seldom considered together in the literature. We emphasize in our analysis how the entropy concept was introduced in response to the desire to extend the applicability of the Second Law to the cosmos at large (Reichenbach and Penrose), and to examine whether intrinsic irreversibility is a fundamental universal characteristics of nature (Prigogine). While the three thinkers operate with vastly different technical proposals and belong to quite distinct intellectual backgrounds, some similarities are detected in their thinking. We philosophically examine these similarities but also bring into focus the uniqueness of each approach. Our purpose is not providing an exhaustive derivations of logical concepts identified in one thinker in terms of ideas found in the others. Instead, the main objective of this work is to stimulate historico-philosophical investigations and inquiries into the problem of the direction of time in nature by way of crossdisciplinary examinations of previous theories commonly treated in literature as disparate domains.

Performance on the processing portion of complex working memory span tasks is related to working memory capacity estimates.

Individual differences in working memory capacity (WMC) have long been known to relate to performance in domains outside of WM, including attentional control, long-term memory, problem-solving, and fluid intelligence to name a few. Complex span WM tasks, composed of a processing component and a storage component, are often used to index WMC in these types of investigations. Capacity estimates are derived from performance on the storage component only, while processing performance is often largely ignored. Here, we explore the relationship between processing performance and WMC in a large dataset for each of three complex span tasks to better characterize how the components of these tasks might be related. We provide evidence that enforcing an 85% or better accuracy criterion for the processing portion of the task results in the removal of a disproportionate number of individuals exhibiting lower WMC estimates. We also find broad support for differences in processing task performance, characterized according to both accuracy and reaction time metrics, as a function of WMC. We suggest that researchers may want to include processing task performance measures, in addition to capacity estimates, in studies using complex span tasks to index WMC. This approach may better characterize the relationships between complex span task performance and performance in disparate domains of cognition.

Interpretable Actions: Controlling Experts with Understandable Commands

Despite the prevalence of deep neural networks, their single most cited drawback is that, even when successful, their operations are inscrutable. For many applications, the desired outputs are the composition of externally-defined bases. For such decomposable domains, we present a two-stage learning procedure producing combinations of the external bases which are trivially extractable from the network. In the first stage, the set of external bases that will form the solution are modeled as differentiable generator modules, controlled by the same parameters as the external bases. In the second stage, a controller network is created that selects parameters for those generators, either successively or in parallel, to compose the final solution. Through three tasks, we concretely demonstrate how our system yields readily understandable commands. In one, we introduce a new form of artistic style transfer, learning to draw and color with crayons, in which the transformation of a photograph or painting occurs not as a single monolithic computation, but by the composition of thousands of individual, visualizable strokes. The other two tasks, single-pass function approximation with arbitrary bases and shape-based synthesis, show how our approach produces understandable and extractable actions in two disparate domains.

The missing link between medical science knowledge and public awareness: implications for tourism and hospitality recovery after COVID-19

PurposeAs the world grapples with the pervasive effects of the coronavirus pandemic, a notable disconnect has emerged in the public's understanding of scientific and medical research. Particularly, the travel industry has become unquestionably vulnerable amid the COVID-19 outbreak; this pandemic has interrupted the industry's operations with devastating economic consequences. This paper aims to highlight the importance of deconstructing barriers between medical science and public awareness related to COVID-19, taking tourism as a case in point. It also discusses the role of interdisciplinary research in facilitating the tourism and hospitality industry's recovery and alleviating tourists' uncertainties in the wake of COVID-19.Design/methodology/approachThis paper offers a synthesis of news coverage from several media outlets, framed within the literature on knowledge transformation across disciplines. This framing focuses on the medical sciences (e.g. public health) and social sciences (e.g. tourism management) to identify gaps between medical scientific knowledge and public awareness in the context of COVID-19. The authors' experience in public health and tourism management further demonstrates a missing link between academic research and the information made available in public health and everyday settings. A potential research agenda is proposed accordingly.FindingsThis paper summarizes how salient issues related to knowledge transfer can become intensified during a global pandemic, such as medical research not being communicated in plain language, which leads some citizens to feel apathetic about findings. Reporting on the prevalence and anticipated consequences of disease outbreaks can hence be difficult, especially early in the development of diseases such as COVID-19.Research limitations/implicationsBy assuming a cross-disciplinary perspective on medical/health and social science research, this paper encourages academic and practical collaboration to bring medical research to the masses. This paper also outlines several research directions to promote public health, safety and sustainability through tourism.Practical implicationsThis paper highlights that it is essential for medical knowledge to be disseminated in a manner that promotes public understanding. The tourism and hospitality industry can benefit from an essential understanding of medical findings, particularly during this pandemic. Without a firm grasp on COVID-19's origins and treatment, the tourism and hospitality industry will likely struggle to recover from this catastrophe.Social implicationsTaking COVID-19 as a case in point, this study advocates leveraging the strengths of disparate domains to bring medical findings to a wider audience and showcase cutting-edge developments for the greater good. This study also emphasizes the importance of engaging the general public in reputable scientific research findings to increase public awareness in a professional and accurate manner.Originality/valueThis paper presents a unique and critical discussion of the gap between medical science knowledge and public awareness, as well as its implications for tourism and hospitality recovery after COVID-19, with a focus on applying medical scientific knowledge to post-pandemic industry recovery.

Visual category learning: Navigating the intersection of rules and similarity.

Visual categorization is fundamental to expertise in a wide variety of disparate domains, such as radiology, art history, and quality control. The pervasive need to master visual categories has served as the impetus for a vast body of research dedicated to exploring how to enhance the learning process. The literature is clear on one point: no category learning technique is always superior to another. In the present review, we discuss how two factors moderate the efficacy of learning techniques. The first, category similarity, refers to the degree of featural overlap of exemplars. The second moderator, category type, concerns whether the features that define category membership can be mastered through learning processes that areimplicit/non-verbal (information-integration categories) or explicit/verbal (rule-based categories). The literature on each moderator has been conducted almost entirely in isolation, such that their potential interaction remains underexplored. We address this gap in the literature by reviewing empirical and theoretical evidence that these two moderators jointly influence the efficacy of learning techniques.

Authenticity: Meanings, targets, audiences and third parties

Research shows that perceived authenticity conveys value in many disparate domains. The analytical attention of this research focuses on producers of products and services, identifying which actions and structures the typical individual associates with authenticity. Far less is known about how individuals and audiences differ in their interest, receptiveness and response to potentially authentic entities and services. Even less is known about how regulators, certifiers, critics and other third parties play a role in the social construction of authenticity. Yet the perception and valuation of a product or service as authentic rests largely with heterogeneous audiences and interpretive third parties. Accordingly, in this chapter, we review and develop theory and empirical research about how targeted entities (producers, persons, products, services), audiences and third parties combine to produce authenticity. For targets, we examine the range of actions and structures of various entities that have been empirically associated with authenticity. For audiences, we explore variations in interests in authenticity based on domain engagement, cosmopolitanism, and cross-cultural differences. For third parties, we consider the roles of other audience members, certifiers and regulators. Finally, we conceptualize a co-evolutionary process whereby targets, audiences and third parties combine to generate social pockets where authenticity is recognized and highly valued.

The Bruce Willis of sandwiches: the Y of X is Y of Z construction on its journey towards a paragon model, as one way of achieving intersubjectivity

The present article is concerned with innovative metaphorical expressions with proper nouns in a subtype of so-caled XYZ constructions, such as Is sad singleton Vladimir Putin the Jennifer Aniston of European politics?, which link relatively disparate domains and which may cause interpretation difficulties, leading to the problem known as de Saussure’s paradox. Veale (2014) believes that they are “double-edged comparisons that can cut both ways, since information inevitably flows in both directions, …. to meet in the middle to construct a blended mental image.” We argue that such constructions are better explained as metaphors based on metonymic paragon models. A closer look at these constructions reveals that they are most of the time used in discourse in such a way that the speaker provides mapping-like statements justifying the pairing of X and Y: speakers create remarkable links between disparate domains on purpose, but they also attempt, in observance of the Gricean principle of cooperation, to facilitate the processing of such expressions and deliberately explicate what seem to be underlying mappings in keeping with the maxim of quantity until supplying enough information to safeguard the interpretation of the figurative expression in the direction of what they have in mind, i.e. ensuring intersubjectivity. In light of similar constructions without any explications, which can be analyzed as based on metonymic paragon models, we realize that spelling out the reasons for the pairings of proper nouns actually seems to function as a trigger for the online creation of novel paragons or quasi-paragons, or for an ad hoc modification of existing paragons. We claim that it is actually these explications that “cut both ways,” i.e. apply to both X and Y, which means that we witness a dynamic (re)construal of meaning whereby a paragon model is strengthened or modified, or is being created.

How can crowdsourcing help tackle the COVID‐19 pandemic? An explorative overview of innovative collaborative practices

The COVID‐19 pandemic has caused unprecedented public health and economic crises. As a response to face the current emergency, science and innovation communities are realizing a fundamental contribution to tackle the crisis. During the past few months, we have witnessed an impressive number of initiatives to encourage networking opportunities, to foster interactions between the different stakeholders involved (health care, industry, governments, academics, ordinary people), and to develop innovative solutions and collaborative infrastructures in support of the health sector. Adopting an open and collaborative approach and joining forces is essential in the fight against the COVID‐19 crisis. Also, the involvement of crowds as innovation partners can be of great support. Therefore, our work aims to review and classify those initiatives, based on the crowdsourcing model, that have been put into place to face the emergency generated by the novel coronavirus pandemic. We illustrate the 16 crowdsourcing initiatives devoted to the SARS‐CoV‐2 outbreak that we identified, detailing their development and implementation. Then, we propose a classification of them, along two dimensions: type of crowdsourcing configuration and kind of tasks, being able to find a relationship between these two aspects. Evidence from the analyzed projects suggests that across disparate domains, crowdsourcing can be an effective strategy in the response to the COVID‐19 pandemic. To conclude, we suggest some important implications for innovation best practices and lessons that can be learned for the future: crowdsourcing, harnessing the power of crowds and online communities, can help tackle the COVID‐19 pandemic, by providing original, actionable, quick, and low‐cost solutions to the challenges of the current health and economic crisis.

Good Cues to Learn From Scratch a Confidence Measure for Passive Depth Sensors

As reported in the stereo literature, confidence estimation represents a powerful cue to detect outliers as well as to improve depth accuracy. Purposely, we proposed a strategy enabling us to achieve state-of-the-art results by learning a confidence measure in the disparity domain only with a CNN. Since this method does not require the cost volume, it is very appealing because potentially suited for any depth-sensing technologies, including, for instance, those based on deep networks. By following this intuition, in this paper, we deeply investigate the performance of confidence estimation methods, known in the literature and new ones proposed in this paper, neglecting the use of the cost volume. Specifically, we estimate from scratch confidence measures feeding deep networks with raw depth estimates and optionally images and assess their performance deploying three datasets and three stereo algorithms. We also investigate, for the first time, their performance with disparity maps inferred by deep stereo end-to-end architectures. Moreover, we move beyond the stereo matching context, estimating confidence from depth maps generated by a monocular network. Our extensive experiments with different architectures highlight that inferring confidence prediction from the raw reference disparity only, as proposed in our previous work, is not only the most versatile solution but also the most effective one in most cases.

Conspiracy in the time of corona: automatic detection of emerging COVID-19 conspiracy theories in social media and the news.

Rumors and conspiracy theories thrive in environments of low confidence and low trust. Consequently, it is not surprising that ones related to the COVID-19 pandemic are proliferating given the lack of scientific consensus on the virus’s spread and containment, or on the long-term social and economic ramifications of the pandemic. Among the stories currently circulating in US-focused social media forums are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frameworks supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread.

Epidemiology of Pediatric Surgery in the United States.

The epidemiology of pediatric surgery in the United States and whether disparities in access to surgical care exist on a national level remain inadequately described. We determined rates of surgical intervention and associations with sociodemographic factors among children 0-17years of age in the United States. Analysis of the 2005-2018 National Health Interview Survey samples included 155,064 children. Parents reported on whether their child had a surgery or surgical procedure either as an inpatient or outpatient over the past 12months. Multivariate logistic regression models, adjusted for age, sex, race and ethnicity, income, language, parent education, region, having a usual source of care, and comorbid conditions, examined odds ratios for sociodemographic factors associated with surgery, analyzing the most recent data (2016-2018; 25544 children). In the most recent data, 4.7% of children had surgical intervention each year, with an average of 3.9 million surgeries performed annually. Rates of surgery were stable between 2005 and 2018. Minority children had lower adjusted odds (aOR) of surgical intervention as compared to white, non-Hispanic children (aOR=0.6, 95%CI=0.5-0.8 for black children, and aOR=0.7, 95%CI=0.5-0.9 for Hispanic children). Other sociodemographic factors associated with a lower adjusted odd of surgical intervention included uninsured status (aOR=0.5; 95%CI=0.3-0.9), and primary language other than English (aOR=0.5; 95%CI 0.3-0.9). Income was not associated with surgical intervention. On average, 3.9 million surgeries are performed on children 0-17years of age in the United States each year. Significant disparities exist in surgical care for children, with black and Hispanic children having lower rates of surgery over and above contribution of other disparity domains. These findings in a nationally representative sample highlight the need for national policies to eliminate disparity of care received by minority children.

‘Come here, it sang, listen’: Juliana Spahr’s commons poetics in That Winter the Wolf Came

ABSTRACT The work of contemporary American poet, critic, and essayist Juliana Spahr is concerned with politically charged questions of connection, collectivity, and community across disparate ontological domains and ecological boundaries. This essay argues that Spahr’s 2015 poetry collection That Winter the Wolf Came, which has to date garnered less critical attention than her previous work, combines lyric, intertextual reference, asequential narrative, parataxis, and other formal techniques and compositional practices into a commons poetics. This poetics, in turn, allows Spahr to effectively represent a commons which finds space for plural subjects as diverse as the activists of the 2011–2012 Occupy Movement, oil rigs, and Brent geese. Despite Spahr’s own fundamental reservations about the political efficacy of poetry, her unique use of lyric, in particular, cultivates radical connections and oppositional tactics for these plural subjects, offering a vision of a utopian commons beyond capitalism.

CESS-A System to Categorize Bangla Web Text Documents

Technology has evolved remarkably, which has led to an exponential increase in the availability of digital text documents of disparate domains over the Internet. This makes the retrieval of the information a very much time- and resource-consuming task. Thus, a system that can categorize such documents based on their domains can truly help the users in obtaining the required information with relative ease and also reduce the workload of the search engines. This article presents a text categorization system (CESS) that categorizes text document using newly proposed hybrid features that combines term frequency-inverse document frequency-inverse class frequency and modified chi-square methods. Experiments were performed on real-world Bangla documents from eight domains comprises of 24,29,857 tokens, and the highest accuracy of 99.91% has been obtained with multilayer perceptron-based classification. Also, the experiments were tested on Reuters-21578 and 20 Newsgroups datasets and obtained accuracies of 97.29% and 94.67%, respectively, to show the language-independent nature of the system.

Optimal Resource Allocation of Cloud-Based Spark Applications

Nowadays, the big data paradigm is consolidating its central position in the industry, as well as in society at large. Lots of applications, across disparate domains, operate on huge amounts of data and offer great advantages both for business and research. According to analysts, cloud computing adoption is steadily increasing to support big data analyses and Spark is expected to take a prominent market position for the next decade. As big data applications gain more and more importance over time and given the dynamic nature of cloud resources, it is fundamental to develop an intelligent resource management system to provide Quality of Service guarantees to end-users. This article presents a set of run-time optimization-based resource management policies for advanced big data analytics. Users submit Spark applications characterized by a priority and by a hard or soft deadline. Optimization policies address two scenarios: i) identification of the minimum capacity to run a Spark application within the deadline; ii) re-balance of the cloud resources in case of heavy load, minimising the weighted soft deadline application tardiness. The solution relies on an initial non-linear programming model formulation and a search space exploration based on simulation-optimization procedures. Spark application execution times are estimated by relying on a gamut of techniques, including machine learning, approximated analyses, and simulation. The benefits of the approach are evaluated on Microsoft Azure HDInsight and on a private cloud cluster based on POWER8 by considering the TPC-DS industry benchmark and SparkBench. The results obtained in the first scenario demonstrate that the percentage error of the prediction of the optimal resource usage with respect to system measurement and exhaustive search is in the range 4–29 percent while literature-based techniques present an average error in the range 6–63 percent. Moreover, in the second scenario, the proposed algorithms can address complex problems like computing the optimal redistribution of resources among tens of applications in less than a minute with an error of 8 percent on average. On the same considered tests, literature-based approaches obtain an average error of about 57 percent.

Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations.

The brain is the most important organ of the human body, and the conversations between the brain and an apparatus can not only reveal a normally functioning or a dysfunctional brain but also can modulate the brain. Here, the apparatus may be a nonbiological instrument, such as a computer, and the consequent brain-computer interface is now a very popular research area with various applications. The apparatus may also be a biological organ or system, such as the gut and muscle, and their efficient conversations with the brain are vital for a healthy life. Are there any common bases that bind these different scenarios? Here, we propose a new comprehensive cross area: Bacomics, which comes from brain-apparatus conversations (BAC) + omics. We take Bacomics to cover at least three situations: (1) The brain is normal, but the conversation channel is disabled, as in amyotrophic lateral sclerosis. The task is to reconstruct or open up new channels to reactivate the brain function. (2) The brain is in disorder, such as in Parkinson's disease, and the work is to utilize existing or open up new channels to intervene, repair and modulate the brain by medications or stimulation. (3) Both the brain and channels are in order, and the goal is to enhance coordinated development between the brain and apparatus. In this paper, we elaborate the connotation of BAC into three aspects according to the information flow: the issue of output to the outside (BAC-1), the issue of input to the brain (BAC-2) and the issue of unity of brain and apparatus (BAC-3). More importantly, there are no less than five principles that may be taken as the cornerstones of Bacomics, such as feedforward and feedback control, brain plasticity, harmony, the unity of opposites and systems principles. Clearly, Bacomics integrates these seemingly disparate domains, but more importantly, opens a much wider door for the research and development of the brain, and the principles further provide the general framework in which to realize or optimize these various conversations.

Unknowability in Climate Science: Chaos, Structure, and Society

Unknowability in Climate Science:Chaos, Structure, and Society Gavin A. Schmidt (bio) CLIMATE SCIENCE AS A DISCIPLINE We are the Borg. You will be assimilated. —The Borg (Star Trek: Voyager 1999) The field of climate science is a relatively new discipline that has arisen from the understanding that the previously distinct fields of meteorology, oceanography, hydrology, studies of the cryosphere, atmospheric chemistry, and such are fundamentally linked and that changes in one or another involve interactions and feedbacks across all these domains. This has always been known at a basic level, but due to the complexity of the overall system, the number of scientists working across these domains was relatively small until recent decades. With new understandings of coupled atmosphere/ocean variability of phenomena like the El Niño–Southern Oscillation, and the growing appreciation of the magnitude of human-caused climate change that emerged in the 1980s, the concentration of scientists working on climate, per se, as opposed to the previously disparate domains, has increased enormously. The challenges within the newly emerged discipline are substantial. Events happening at the smallest spatial and fastest temporal scales—for instance, the nucleation of cloud-condensing nuclei at the micrometer/microsecond scales—are as important to climate as [End Page 133] the multi-millennial global shifts in the orbital parameters for Earth. Integrating understanding across such vast scales (some 14 orders of magnitude in time and space) is technically challenging and, because there is so much ground to cover, very difficult to synthesize into a single coherent theory. Additionally, the enormity of the observational data now available from weather stations, ocean buoys, and, importantly, remote sensing by satellites defies simple characterization. Even data from the past—whether old instrumental records or proxies for climate changes in the ice, trees, or ocean sediments—require complex processing before they can be correctly interpreted. Complexity is thus the background upon which all work in climate science is situated, and the increasing tractability of complex problems over time has both made this work necessary and driven it forward. THE NECESSITY FOR CLIMATE MODELS Si Dieu n'existait pas, il faudrait l'inventer. —Voltaire (1768) The advances in computational processing have allowed the interconnections to be quantified and processed. Physics-based, multi-process, multi-domain general circulation models (GCMs) have steadily become more advanced, more complex, and more complete over the last few decades (figure 1). These models are playing an ever-greater role in integrating observations, deepening understanding, and making forecasts (from weather timescales to multiple decades). Although it may sound a little clichéd, the substantial role that large-scale simulation plays in climate science now, compared to 30 or 40 years ago, can be usefully described as a new paradigm (Kuhn 1962). But what has driven this change? Certainly, it is related to the enormous improvements in computational capacity and accessibility over the last 50 years that have made once inconceivable calculations [End Page 134] routine. However, the biggest driver is the success of early efforts that demonstrated that not only were old questions answered in better ways, but also new questions could be posed that would have been impossible to address before. Climate models have enabled better understanding, improved attribution, and skillful prediction. More relevant for this paper, they have also clarified the limits to that knowledge. Click for larger view View full resolution Figure 1. History of climate modeling showing the progressive integration of previously separate efforts over the last 60 years (modified from CarbonBrief 2018). Climate science as a whole is much broader than the development and application of these models, clearly. But these models form the backdrop for all substantive prediction efforts in weather and climate. Their limitations are an excellent starting point for considering where the limits of knowledge in climate science lie and how much of what is beyond those limits is truly unknowable as opposed to simply, as yet, unknown. The construction, calibration, and application of these models are complex (Schmidt and Sherwood 2015; Schmidt et al. 2017), but a number of points are clear. First, no single model is (or will ever be) either complete or perfect. There will always be processes that need to be approximated...

A Chebyshev polynomial feedforward neural network trained by differential evolution and its application in environmental case studies

This paper introduces a polynomial feedforward neural network based on Chebyshev polynomials able to effectively model non-linear and highly complex environmental data. The data sets were cautiously selected from the fields of biology, ecology, climate, and environmental management, and economics as to represent a scientifically meaningful and consistent corpus of disparate domains of intensive focus and interest in current ecological/environmental research, covering issues related to body growth/age, biomass production, energy efficiency/consumption, and ecology/geographic extension. The proposed network uses a number of layers to estimate the output in terms of a weighted sum of truncated Chebyshev series expansions applied to linear combinations of the input variables, and it is trained by the differential evolution algorithm. Its performance was compared to three neural networks. First, a polynomial feedforward network that uses Hermite polynomials as activation function in the hidden nodes; second, a radial basis function neural network; third, a Takagi-Sugeno-Kang neuro-fuzzy network. All the above networks were trained by evolutionary optimization algorithms. The comparison was carried out by standard criteria such as the root mean square error and the mean absolute error. Moreover, a non-parametric Kruskal-Wallis statistical test used to compare the median values of the root mean square errors between methods. The main experimental outcomes are: (a) the network's efficiency improves for higher polynomial orders, (b) the statistical analysis suggests that the proposed network appears to be very competitive to the other three networks.

From Music to Medicine: Are Pianists at an Advantage When Learning Surgical Skills?

Background: The acquisition of procedural competence is of vital importance in the training of physicians. It has been observed that medical students with extensive musical backgrounds often learn surgical techniques more rapidly than other students, raising the question of motor skill transfer from one area to another. Objective: It is the aim of this project to explore whether musicians can learn and perform surgical skills more rapidly than non-musicians. This study explores the claims that musicians’ proficiency in playing their instrument can translate into benefits when learning complex and refined motor skills in another domain. Even basic surgical skills, such as suturing, become difficult in cognitively demanding environments such as the operating room, containing a barrage of multisensory stimuli where the surgeon must triage and respond to clinically salient information. Method: Participants with piano expertise and participants with no formal music training learned how to do a surgical knot and sutures. They had two practice sessions and were tested after each session. The two test parameters measured were time to complete the task and an OSATS (Objective Structures Assessment of Technical Skills) score. Results for each group (musicians and non-musicians) were analysed and compared. Results: Musician participants performed the surgical tasks faster and received higher scores than the controls; for knot tying, the difference between the two groups was statistically significant. Gender and proficiency using chopsticks also exhibited some influence on test times and scores. Conclusion: Musical training in piano appeared to be of benefit in the initial stage of learning new simple surgical skills. This indicates that at least some aspects of a musicians’ skillset (such as fine motor control, bimanual dexterity and good hand-eye coordination) might be transferrable to an ostensibly disparate domain, and may be important for incorporation in surgical training where the skill of suturing can impact both surgical outcomes, patient safety, and patient satisfaction.

Learning a confidence measure in the disparity domain from O(1) features

Depth sensing is of paramount importance for countless applications and stereo represents a popular, effective and cheap solution for this purpose. As highlighted by recent works concerned with stereo, uncertainty estimation can be a powerful cue to improve accuracy in stereo. Most confidence measures rely on features, mainly extracted from the cost volume, fed to a random forest or a convolutional neural network trained to estimate match uncertainty. In contrast, we propose a novel strategy for confidence estimation based on features computed in the disparity domain, making our proposal suited for any stereo system including COTS devices, and in constant time. We exhaustively assess the performance of our proposals, referred to as O1 and O2, on KITTI and Middlebury datasets with three popular and different stereo algorithms (CENSUS, MC-CNN and SGM), as well as a deep stereo network (PSM-Net). We also evaluate how well confidence measures generalize to different environments/datasets.