Detecting Large Concept Extensions for Conceptual Analysis

When performing a conceptual analysis of a concept, philosophers are interested in all forms of expression of a concept in a text---be it direct or indirect, explicit or implicit. In this paper, we experiment with topic-based methods of automating the detection of concept expressions in order to facilitate philosophical conceptual analysis. We propose six methods based on LDA, and evaluate them on a new corpus of court decision that we had annotated by experts and non-experts. Our results indicate that these methods can yield important improvements over the keyword heuristic, which is often used as a concept detection heuristic in many contexts. While more work remains to be done, this indicates that detecting concepts through topics can serve as a general-purpose method for at least some forms of concept expression that are not captured using naive keyword approaches

Designing visual analytics methods for massive collections of movement data

Exploration and analysis of large data sets cannot be carried out using purely visual means but require the involvement of database technologies, computerized data processing, and computational analysis methods. An appropriate combination of these technologies and methods with visualization may facilitate synergetic work of computer and human whereby the unique capabilities of each “partner” can be utilized. We suggest a systematic approach to defining what methods and techniques, and what ways of linking them, can appropriately support such a work. The main idea is that software tools prepare and visualize the data so that the human analyst can detect various types of patterns by looking at the visual displays. To facilitate the detection of patterns, we must understand what types of patterns may exist in the data (or, more exactly, in the underlying phenomenon). This study focuses on data describing movements of multiple discrete entities that change their positions in space while preserving their integrity and identity. We define the possible types of patterns in such movement data on the basis of an abstract model of the data as a mathematical function that maps entities and times onto spatial positions. Then, we look for data transformations, computations, and visualization techniques that can facilitate the detection of these types of patterns and are suitable for very large data sets – possibly too large for a computer's memory. Under such constraints, visualization is applied to data that have previously been aggregated and generalized by means of database operations and/or computational techniques

Attention-deficit/hyperactivity disorder (ADHD) symptoms and academic entrepreneurial preference: is there an association?

Although commercialization of research activities has drawn some research attention, more studies are warranted to clearly understand the drivers behind academic entrepreneurship. The present paper investigates the association between attention-deficit/hyperactivity disorder (ADHD) symptoms and academic entrepreneurial preference. ADHD symptoms have typically been associated with impaired occupational functioning among wage employees. Recent studies, however, indicate that the same symptoms of ADHD that are a liability for wage employees may work out differently for entrepreneurs. Building on previous studies that link ADHD symptoms to entrepreneurship, and using the theoretical lens of person-environment fit, we hypothesize that ADHD symptoms (at the so-called subclinical level) are associated with academic entrepreneurial preference. Results of our data from academic researchers in France, Spain, and Italy (N = 534) show that there is a negative association between attention-deficit symptoms and academic entrepreneurial preference. However, there is no link between hyperactivity symptoms and academic entrepreneurial preference

European consensus statement on diagnosis and treatment of adult ADHD: The European Network Adult ADHD.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is among the most common psychiatric disorders of childhood that persists into adulthood in the majority of cases. The evidence on persistence poses several difficulties for adult psychiatry considering the lack of expertise for diagnostic assessment, limited treatment options and patient facilities across Europe. METHODS: The European Network Adult ADHD, founded in 2003, aims to increase awareness of this disorder and improve knowledge and patient care for adults with ADHD across Europe. This Consensus Statement is one of the actions taken by the European Network Adult ADHD in order to support the clinician with research evidence and clinical experience from 18 European countries in which ADHD in adults is recognised and treated. RESULTS: Besides information on the genetics and neurobiology of ADHD, three major questions are addressed in this statement: (1) What is the clinical picture of ADHD in adults? (2) How can ADHD in adults be properly diagnosed? (3) How should ADHD in adults be effectively treated? CONCLUSIONS: ADHD often presents as an impairing lifelong condition in adults, yet it is currently underdiagnosed and treated in many European countries, leading to ineffective treatment and higher costs of illness. Expertise in diagnostic assessment and treatment of ADHD in adults must increase in psychiatry. Instruments for screening and diagnosis of ADHD in adults are available and appropriate treatments exist, although more research is needed in this age group

Forecasting and Operational Research:A Review

From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial