Policy Learning for Individualized Treatment Regimes on Infinite Time Horizon

With the recent advancements of technology in facilitating real-time monitoring and data collection, "just-in-time" interventions can be delivered via mobile devices to achieve both real-time and long-term management and control. Reinforcement learning formalizes such mobile interventions as a sequence of decision rules and assigns treatment arms based on the user's status at each decision point. In practice, real applications concern a large number of decision points beyond the time horizon of the currently collected data. This usually refers to reinforcement learning in the infinite horizon setting, which becomes much more challenging. This article provides a selective overview of some statistical methodologies on this topic. We discuss their modeling framework, generalizability, and interpretability and provide some use case examples. Some future research directions are discussed in the end

Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

Model-driven design of context-aware applications

In many cases, in order to be effective, software applications need to allow sensitivity to context changes. This implies however additional complexity associated with the need for applications’ adaptability (being capable of capturing context, interpreting it and reacting on it). Hence, we envision 3 ‘musts’ that, in combination, are especially relevant to the design of context-aware applications. Firstly, at the business modeling level, it is considered crucial that the different possible context states can be properly captured and modeled, states that correspond to certain desirable behaviors. Secondly, it must be known what are the dependencies between the two, namely between states and behaviors. And finally, what is valid for application design in general, business needs are to be aligned to application solutions. In this work, we address the mentioned challenges, by approaching the notion of context and extending from this perspective a previously proposed business-software alignment approach. We illustrate our achieved results by means of a small example. It is expected that this research contribution will be useful as an additional result concerning the alignment between business modeling and software design

Comment: Classifier Technology and the Illusion of Progress--Credit Scoring

Comment on Classifier Technology and the Illusion of Progress--Credit Scoring [math.ST/0606441]Comment: Published at http://dx.doi.org/10.1214/088342306000000051 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modeling Empathy and Distress in Reaction to News Stories

Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations: It underestimates the psychological complexity of the phenomenon, adheres to a weak notion of ground truth where empathic states are ascribed by third parties, and lacks a shared corpus. In contrast, this contribution presents the first publicly available gold standard for empathy prediction. It is constructed using a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. This is also the first computational work distinguishing between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. Finally, we present experimental results for three different predictive models, of which a CNN performs the best.Comment: To appear at EMNLP 201

How does observational learning produce placebo effects? : a model integrating research findings

There is a growing body of evidence proving that observational learning, in addition to classical conditioning and verbal suggestions, may induce both placebo analgesia and nocebo hyperalgesia. However, much less is known about the mechanisms and factors influencing placebo effects induced by observational learning. The paper critically reviews the research findings in the field in the context of Bandura’s social learning theory. We apply Bandura’s taxonomy of the sources of social learning (behavioral, symbolic, and verbal modeling) and discuss the results of previous studies. Critical points in the placebo effects induced by observational learning are identified.We discuss aspects of behavior presented by the model (both verbal and nonverbal) involved in the formation of placebo effects induced by observational learning as well as the role of expectancies in this process. As a result, we propose a model that integrates the existing research findings. The model shows the main ways of transmission of painrelated information from the model to the observer. It highlights the role of expectancies and the individual characteristics of the observer in formation of placebo analgesia and nocebo hyperalgesia induced by observational learning. Finally, we propose future research directions based on our model

Metamodel for Tracing Concerns across the Life Cycle

Several aspect-oriented approaches have been proposed to specify aspects at different phases in the software life cycle. Aspects can appear within a phase, be refined or mapped to other aspects in later phases, or even disappear.\ud Tracing aspects is necessary to support understandability and maintainability of software systems. Although several approaches have been introduced to address traceability of aspects, two important limitations can be observed. First, tracing is not yet tackled for the entire life cycle. Second, the traceability model that is applied usually refers to elements of specific aspect languages, thereby limiting the reusability of the adopted traceability model.We propose the concern traceability metamodel (CTM) that enables traceability of concerns throughout the life cycle, and which is independent from the aspect languages that are used. CTM can be enhanced to provide additional properties for tracing, and be instantiated to define\ud customized traceability models with respect to the required aspect languages. We have implemented CTM in the tool M-Trace, that uses XML-based representations of the models and XQuery queries to represent tracing information. CTM and M-Trace are illustrated for a Concurrent Versioning System to trace aspects from the requirements level to architecture design level and the implementation

From Comparative Risk to Decision Analysis: Ranking Solutions to Multiple-Value Environmental Problems

While recognizing that the making of environmental policy is sufficiently complex that no one method can serve all conditions, Dr. Kadvany urges that more attention be given to multiattribute utility and decision analysis. He suggests this can help, e.g., to illuminate stakeholder values and generate alternative approaches