A Theory of the Multiple Roles of Diagnosis in Collaborative Problem Solving Discourse

A better understanding of the nature of consultations between professionals engaging in the collaborative process of solving complex problems — expertise in use — offers the potential to reshape our ideas about how to design computer systems that can engage in collaborative problem solving with their human cohorts. The research reported here has sought to account for key behaviors contributing to successful consultation, as identified by a cognitive task assessment of human-human consultation discourse in the medical teaching rounds setting. W e have come to view the communication acts of the presenter/investigator as evidence of his deliberate intention to indirectly construct a particular model of the patient's case — his model — in the expert's mind, resulting in two separate but related diagnostic tasks for the expert: one at the patient level and one at the presenter/investigator level. This dual-diagnostic theory of expert understanding of the presenter/investigator's communication actions is partially implemented in the RUMINATE program. The theory provides insights into the expert's capacity to model aspects of the presenter/investigator's competence — insights that contribute to our understanding of expertise embedded in the context of collaborative problem solving discourse

Architecture and engineering of a supramolecular functional material by manipulating the nano-structure of fiber network

Three-dimensional fiber networks were created from an organogel system consisting mainly of elongated fibrils by using a nonionic surfactant as an additive. The presence of the surfactant molecules manipulates the network structure by enhancing the mismatch nucleation on the growing fiber tips. Both the fiber network structure and the rheological properties of the material can be finely tuned by changing the surfactant concentration, which provides a robust approach to the engineering of supramolecular soft functional materials.<br /

Competencies for bibliometrics

Universities are increasingly offering support services for bibliometrics, often based in the library. This paper describes work done to produce a competency model for those supporting bibliometrics. The results of a questionnaire in which current practitioners rated bibliometric tasks as entry level, core or specialist are reported. Entry level competencies identified were explaining bibliometric concepts, doing basic calculations and some professional skills. Activities identified by participants as core are outlined. Reflecting on items that were considered in scope but specialist there was less stress on evaluating scholars, work at a strategic level, working with data outside proprietary bibliometric tools and consultancy-type services as opposed to training for disintermediated use. A competency model is presented as an appendix

Exploring the role of GIS during community health assessment problem solving: experiences of public health professionals

BACKGROUND: A Community health assessment (CHA) involves the use of Geographic Information Systems (GIS) in conjunction with other software to analyze health and population data and perform numerical-spatial problem solving. There has been little research on identifying how public health professionals integrate this software during typical problem solving scenarios. A better understanding of this is needed to answer the "What" and the "How". The "What" identifies the specific software being used and the "How" explains the way they are integrated together during problem solving steps. This level of understanding will highlight the role of GIS utilization during problem solving and suggest to developers how GIS can be enhanced to better support data analysis during community health assessment. RESULTS: An online survey was developed to identify the information technology used during CHA analysis. The tasks were broken down into steps and for our analysis these steps were categorized by action: Data Management/Access, Data Navigation, Geographic Comparison, Detection of Spatial Boundaries, Spatial Modelling, and Ranking Analysis. 27 CHA professionals completed the survey, with the majority of participants (14) being from health departments. Statistical software (e.g. SPSS) was the most popular software for all but one of the types of steps. For this step (detection of spatial boundaries), GIS was identified as the most popular technology. CONCLUSION: Most CHA professionals indicated they use statistical software in conjunction with GIS. The statistical software appears to drive the analysis, while GIS is used primarily for simple spatial display (and not complex spatial analysis). This purpose of this survey was to thoroughly examine into the process of problem solving during community health assessment data analysis and to gauge how GIS is integrated with other software for this purpose. These findings suggest that GIS is used more for spatial display while other software such as statistical packages (the "What") are used to drive data management, data navigation, and data calculation (the "How"). Focusing at the level of how public health problems are solved, can shed light on how GIS technology can be enhanced to encompass a stronger role during community health assessment problem solving

Daily heat load variations in Swedish district heating systems

ABSTRACT If daily heat load variations could be eliminated in district heating-systems, it would make the operation of the district heating system less costly and more competitive . There would be several advantages in the operation such as:

Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling

This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass. We exploit general representations from foundation models and unlabelled datasets through a Masked Image Modeling (MIM) approach, which is robust to augmentation hyper-parameters and simpler than previous techniques. For neural networks used in safety-critical applications, bias in the training data can lead to errors; therefore it is crucial to understand a network's limitations at run time and act accordingly. To this end, we test our proposed method on a number of test domains including the SAX Segmentation benchmark, which includes labelled test data from dense urban, rural and off-road driving domains. The proposed method consistently outperforms uncertainty estimation and Out-of-Distribution (OoD) techniques on this difficult benchmark.Comment: Accepted for publication at 2024 IEEE International Conference on Robotics and Automation (ICRA

Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data

Knowing when a trained segmentation model is encountering data that is different to its training data is important. Understanding and mitigating the effects of this play an important part in their application from a performance and assurance perspective - this being a safety concern in applications such as autonomous vehicles (AVs). This work presents a segmentation network that can detect errors caused by challenging test domains without any additional annotation in a single forward pass. As annotation costs limit the diversity of labelled datasets, we use easy-to-obtain, uncurated and unlabelled data to learn to perform uncertainty estimation by selectively enforcing consistency over data augmentation. To this end, a novel segmentation benchmark based on the SAX Dataset is used, which includes labelled test data spanning three autonomous-driving domains, ranging in appearance from dense urban to off-road. The proposed method, named Gamma-SSL, consistently outperforms uncertainty estimation and Out-of-Distribution (OoD) techniques on this difficult benchmark - by up to 10.7% in area under the receiver operating characteristic (ROC) curve and 19.2% in area under the precision-recall (PR) curve in the most challenging of the three scenarios.Comment: Accepted for publication in IEEE Transactions on Robotics (T-RO