Positive representations of C0(X)C_0(X). I

We introduce the notion of a positive spectral measure on a $\sigma$-algebra, taking values in the positive projections on a Banach lattice. Such a measure generates a bounded positive representation of the bounded measurable functions. If $X$ is a locally compact Hausdorff space, and $\pi$ is a positive representation of $C_0(X)$ on a KB-space, then $\pi$ is the restriction to $C_0(X)$ of such a representation generated by a unique regular positive spectral measure on the Borel $\sigma$-algebra of $X$. The relation between a positive representation of $C_0(X)$ on a Banach lattice and -- if it exists -- a generating positive spectral measure on the Borel $\sigma$-algebra is further investigated; here and elsewhere phenomena occur that are specific for the ordered context.Comment: There is now a direct proof of the existence of a generating regular positive spectral measure in the case of KB-spaces, without resorting to the Banach space theory. References to the existing literature on the Banach space case have been added, and perspectives for future research are now given. 24 pages, to appear in Ann. Funct. Ana

Effective Natural Language Interfaces for Data Visualization Tools

How many Covid cases and deaths are there in my hometown? How much money was invested into renewable energy projects across states in the last 5 years? How large was the biggest investment in solar energy projects in the previous year? These questions and others are of interest to users and can often be answered by data visualization tools (e.g., COVID-19 dashboards) provided by governmental organizations or other institutions. However, while users in organizations or private life with limited expertise with data visualization tools (hereafter referred to as end users) are also interested in these topics, they do not necessarily have knowledge of how to use these data visualization tools effectively to answer these questions. This challenge is highlighted by previous research that provided evidence suggesting that while business analysts and other experts can effectively use these data visualization tools, end users with limited expertise with data visualization tools are still impeded in their interactions. One approach to tackle this problem is natural language interfaces (NLIs) that provide end users with a more intuitive way of interacting with these data visualization tools. End users would be enabled to interact with the data visualization tool both by utilizing the graphical user interface (GUI) elements and by just typing or speaking a natural language (NL) input to the data visualization tool. While NLIs for data visualization tools have been regarded as a promising approach to improving the interaction, two design challenges still remain. First, existing NLIs for data visualization tools still target users who are familiar with the technology, such as business analysts. Consequently, the unique design required by end users that address their specific characteristics and that would enable the effective use of data visualization tools by them is not included in existing NLIs for data visualization tools. Second, developers of NLIs for data visualization tools are not able to foresee all NL inputs and tasks that end users want to perform with these NLIs for data visualization tools. Consequently, errors still occur in current NLIs for data visualization tools. End users need to be therefore enabled to continuously improve and personalize the NLI themselves by addressing these errors. However, only limited work exists that focus on enabling end users in teaching NLIs for data visualization tools how to correctly respond to new NL inputs. This thesis addresses these design challenges and provides insights into the related research questions. Furthermore, this thesis contributes prescriptive knowledge on how to design effective NLIs for data visualization tools. Specifically, this thesis provides insights into how data visualization tools can be extended through NLIs to improve their effective use by end users and how to enable end users to effectively teach NLIs how to respond to new NL inputs. Furthermore, this thesis provides high-level guidance that developers and providers of data visualization tools can utilize as a blueprint for developing data visualization tools with NLIs for end users and outlines future research opportunities that are of interest in supporting end users to effectively use data visualization tools

Designing Multimodal BI&A Systems for Face-to-Face Team Interactions

Organizations increasingly assign complex decision-making tasks to teams. However, current business intelligence & analytics (BI&A) systems are primarily designed to support individual decision-makers and, therefore, cannot be used effectively in face-to-face team interactions. To address this challenge, we conduct a design science research (DSR) project to design a multimodal BI&A system that can be used effectively using a combination of touch and speech interaction. Drawing on the theory of effective use and existing guidelines for multimodal user interfaces, we formulated and instantiated three design principles in an artifact. The results of a focus group evaluation indicate that enhancing the BI&A system with a speech facilitates transparent interaction and increases effective use of the system in team interactions. Our DSR project contributes to the body of design knowledge for multimodal BI&A systems by demonstrating how the combination of touch and speech facilitates its effective use in face-to-face team interactions

The Impact of Conversational Assistance on the Effective Use of Forecasting Support Systems: A Framed Field Experiment

Forecasting support systems (FSSs) support demand planners in important forecasting decisions by offering statistical forecasts. However, planners often rely on their judgment more than on system-based advice which can be detrimental to forecast accuracy. This is caused by a lack of understanding and subsequent lack of trust in the FSS and its advice. To address this problem, we explore the potential of extending the traditional static assistance (e.g., manuals, tooltips) with conversational assistance provided by a conversational assistant that answers planners’ questions. Drawing on the theory of effective use, we aim to conduct a framed field experiment to investigate whether conversational (vs. static) assistance better supports planners in learning the FSS, increases their trust, and ultimately helps them make more accurate forecasting decisions. With our findings, we aim to contribute to research on FSS design and the body of knowledge on the theory of effective use

Designing Conversational Dashboards for Effective Use in Crisis Response

Dashboards are increasingly used by governments and health organizations to provide important information to the general public during a crisis. However, in contrast to organizational settings, the majority of the general population has not or rarely used dashboards before and therefore often struggles to interact effectively with these dashboards. To address this challenge, we conduct a design science research (DSR) project to design a conversational dashboard that enables natural language-based interactions to facilitate its effective use. Drawing on the theory of effective use, our DSR project aims to provide theory-grounded design knowledge for conversational dashboards that help users to access and find information via natural language. Moreover, we seek to provide novel insights that support researchers and practitioners in understanding and designing more natural and effective interactions between users and dashboards

Designing Multimodal BI&A Systems for Co-Located Team Interactions

Teams are crucial for organizations in making data-driven decisions. However, current business intelligence & analytics (BI&A) systems are primarily designed to support individuals and, therefore, cannot be used effectively in co-located team interactions. To address this challenge, we conduct a design science research (DSR) project to design a multimodal BI&A system providing touch and speech interactions that can be used effectively by teams. Drawing on the theory of effective use and existing guidelines for multimodal user interfaces, we propose three design principles and instantiate them in a software artifact. The results of a focus group evaluation indicate that enhancing the BI&A system with multimodal capabilities increases transparent interaction and facilitates effective use of the system in co-located team interactions. Our DSR project contributes novel design knowledge for multimodal BI&A systems with touch and speech modalities that facilitate effective use in co-located team interactions

Information or integration? Supporting multimodal travelling through mobility apps

Digital tools like journey planners and mobile ticketing apps are adopted by more and more users and seen as"br" key enablers to making multimodal travel choices easier. This paper looks at success factors for establishing a"br" highly integrated mobility app. By analysing and comparing levels of integration across different axes for"br" mobility apps as well as geographical coverage and user adoption figures, we identify common types and"br" evolution paths. The findings suggest that vertically integrated apps tend to be limited to one mode and in terms"br" of geographic coverage. Widely used apps often integrate travel information with other functions and have wider"br" coverage, but show low vertical integration levels. If increased multimodal travel behaviour is linked to using"br" mobility apps, then a strategy pursuing wide user adoption may be more successful than building strongly"br" integrated platforms in the first place. This has implications for public and private initiatives looking to build"br" their own app or sharing their data and cooperating with others

Designing Conversational Dashboards for Effective Use in Crisis Response

Governments and health organizations increasingly use dashboards to provide real-time information during natural disasters and pandemics. Although these dashboards aim to make crisis-related information accessible to the general public, the average user can have a hard time interacting with them and finding the information needed to make everyday decisions. To address this challenge, we draw on the theory of effective use to propose a theory-driven design for conversational dashboards in crisis response, which improves users’ transparent interaction and access to crisis-related information. We instantiate our proposed design in a conversational dashboard for the COVID-19 pandemic that enables natural language interaction in spoken or written form and helps users familiarize themselves with the use of natural language through conversational onboarding. The evaluation of our artifact shows that being able to use natural language improves users’ interaction with the dashboard and ultimately increases their efficiency and effectiveness in finding information. This positive effect is amplified when users complete the onboarding before interacting with the dashboard, particularly when they can use both natural language and mouse. Our findings contribute to research on dashboard design, both in general and in the specific context of crisis response, by providing prescriptive knowledge for extending crisis response dashboards with natural language interaction capabilities. In addition, our work contributes to the democratization of data science by proposing design guidelines for making information in crisis response dashboards more accessible to the general public

Super-resolution fluorescence microscopy by line-scanning with an unmodified two-photon microscope

Pilger C, Pospisil J, Müller M, et al. Super-resolution fluorescence microscopy by line-scanning with an unmodified two-photon microscope. Philosophical Transactions of the Royal Society of London, Series A : Mathematical, Physical and Engineering Sciences . 2021;379(2199): 20200300.Fluorescence-based microscopy as one of the standard tools in biomedical research benefits more and more from super-resolution methods, which offer enhanced spatial resolution allowing insights into new biological processes. A typical drawback of using these methods is the need for new, complex optical set-ups. This becomes even more significant when using two-photon fluorescence excitation, which offers deep tissue imaging and excellent z-sectioning. We show that the generation of striped-illumination patterns in two-photon laser scanning microscopy can readily be exploited for achieving optical super-resolution and contrast enhancement using open-source image reconstruction software. The special appeal of this approach is that even in the case of a commercial two-photon laser scanning microscope no optomechanical modifications are required to achieve this modality. Modifying the scanning software with a custom-written macro to address the scanning mirrors in combination with rapid intensity switching by an electro-optic modulator is sufficient to accomplish the acquisition of two-photon striped-illumination patterns on an sCMOS camera. We demonstrate and analyse the resulting resolution improvement by applying different recently published image resolution evaluation procedures to the reconstructed filtered widefield and super-resolved images. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'