AMOLED displays with transfer‐printed integrated circuits

Abstract— Small integrated circuits of crystalline silicon (chiplets) transfer‐printed onto a flat‐panel‐display substrate provide greatly improved electrical performance and uniformity in active‐matrix organic light‐emitting‐diode (OLED) displays. The integrated circuits are formed in high‐performance crystalline silicon using conventional photolithographic processes and then transfer‐printed onto a substrate using a stamp that transfers hundreds or thousands of chiplets at once. The chiplets are connected to an external controller and to pixel elements using conventional photolithographic substrate processing methods. Active‐matrix OLED (AMOLED) displays using transfer‐printed chiplets have good yields, excellent uniformity, and electrical performance and are thermally robust.     

SimKnowledge—Analyzing impact of knowledge management measures on team organizations with multi agent-based simulation

Even though there is abundant literature on successful cases of organizations applying knowledge management (KM) measures, many KM initiatives have failed to achieve their knowledge and business goals. In order to foster decisions about the design of such initiatives, information is required on success factors and barriers when selecting KM measures. Multi agent-based simulation (MABS) is suggested as instrument to investigate potential effects of KM measures on dependent variables such as sharing of knowledge in organizations or business performance. For such a simulation, the concept of knowledge sharing, influencing factors and their impact on business and knowledge goals are modeled based on an extensive multi-disciplinary literature survey. An extensive domain model is operationalized in a simulation model which is then further simplified and implemented in a MABS tool used for a series of experiments contrasting results with/without KM measures, specifically skill and experience management. Skill management is found highly sensitive with respect to conditions of application and has no significant impact on knowledge or business goals. Experience management positively impacts knowledge and business goals. Personal documentation leads to specialist, project debriefings to generalist knowledge workers. Finally, the paper discusses the simulation’s limitations and further areas of application.     

Colonic fold detection from computed tomographic colonography images using diffusion-FCM and level sets

Colon cancer is the second major cause of cancer related deaths in industrial nations. Computed tomographic colonography (CTC) has emerged in the last decade as a new less invasive colon diagnostic alternative to the usually practiced optical colonoscopy. The overall goal is to increase the effectiveness of virtual endoscopic navigation of the existing computer-aided detection (CAD) system. The colonic/haustral folds serve as important landmarks for various associated tasks in the virtual endoscopic navigation like prone–supine registration, colonic polyp detection and tenia coli extraction. In this paper, we present two different techniques, first in isolation and then in synergism, for the detection of haustral folds. Our input is volumetric computed tomographic colonography (CTC) images. The first method, which uses a combination of heat diffusion and fuzzy c-means algorithm (FCM), has a tendency of over-segmentation. The second method, which employs level sets, suffers from under-segmentation. A synergistic combination, where the output of the first is used as an input for the second, is shown to improve the segmentation quality. Experimental results are presented on digital colon phantoms as well as real patient scans. The combined method has a total erroneous (over-segmentation plus under-segmentation) detection of (6.5 ± 2)% of the total number of folds per colon as compared to (12.5 ± 5)% for the diffusion-FCM-based method and (11.5 ± 3)% for the level set-based method. The p-values obtained from the associated ANOVA tests indicate that the performance improvements are statistically significant.     

Visualization of diversity in large multivariate data sets

Understanding the diversity of a set of multivariate objects is an important problem in many domains, including ecology, college admissions, investing, machine learning, and others. However, to date, very little work has been done to help users achieve this kind of understanding. Visual representation is especially appealing for this task because it offers the potential to allow users to efficiently observe the objects of interest in a direct and holistic way. Thus, in this paper, we attempt to formalize the problem of visualizing the diversity of a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth deeper investigation by the information visualization community. In doing so, we contribute a precise definition of diversity, a set of requirements for diversity visualizations based on this definition, and a formal user study design intended to evaluate the capacity of a visual representation for communicating diversity information. Our primary contribution, however, is a visual representation, called the Diversity Map, for visualizing diversity. An evaluation of the Diversity Map using our study design shows that users can judge elements of diversity consistently and as or more accurately than when using the only other representation specifically designed to visualize diversity.     

On the dispersion measure of OWA operators

We describe some basic features of the OWA operator. We turn to the problem of determining the weights associated with this operator and particularly the maximal dispersion (entropy) approach. We consider the possibility of using minimization of dispersion. After discussing concerns with both maximization and minimization of dispersion we investigate the possibility of finding an optimal solution intermediate to these extremes. We next consider alternative measures of dispersion. We introduce a fundamental requirement for a measure of dispersion called the Preference for Equal Division. A number of general classes of dispersion measures are provided notable among these are those based on t-norm and t-conorm operators.     

From Berman and Hafner’s teleological context to Baude and Sachs’ interpretive defaults: an ontological challenge for the next decades of AI and Law

This paper revisits the challenge of Berman and Hafner’s “missing link” paper on representing teleological structure in case-based legal reasoning. It is noted that this was mainly an ontological challenge to represent some of what made legal reasoning distinctive, which was given less attention than factual similarity in the dominant AI and Law paradigm, deriving from HYPO. The response to their paper is noted and briefly evaluated. A parallel is drawn to a new challenge to provide deep structure to the legal context of textual meaning, drawing on the forthcoming work of two Constitutional law scholars who appear to place some faith in the ways of thinking that AI and Law has developed.     

Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agent-based learning environments

The current study examined the relationships between learners’ (\(N = 123\)) personality traits, the emotions they typically experience while studying (trait studying emotions), and the emotions they reported experiencing as a result of interacting with four pedagogical agents (agent-directed emotions) in MetaTutor, an advanced multi-agent learning environment. Overall, significant relationships between a subset of trait emotions (trait anger, trait anxiety) and personality traits (agreeableness, conscientiousness, and neuroticism) were found for four agent-directed emotions (enjoyment, pride, boredom, and neutral) though the relationships differed between pedagogical agents. These results demonstrate that some trait emotions and personality traits can be used to predict learners’ emotions directed toward specific pedagogical agents (with different roles). Results provide suggestions for adapting pedagogical agents to support learners’ (with certain characteristics; e.g., high in neuroticism or agreeableness) experience of adaptive emotions (e.g., enjoyment) and minimize their experience on non-adaptive emotions (e.g., boredom). Such an approach presents a scalable and easily implementable method for creating emotionally-adaptive, agent-based learning environments, and improving learner-pedagogical agent interactions in order to support learning.