Towards Nootropia : a non-linear approach to adaptive document filtering

In recent years, it has become increasingly difficult for users to find relevant information within the accessible glut. Research in Information Filtering (IF) tackles this problem through a tailored representation of the user interests, a user profile. Traditionally, IF inherits techniques from the related and more well established domains of Information Retrieval and Text Categorisation. These include, linear profile representations that exclude term dependencies and may only effectively represent a single topic of interest, and linear learning algorithms that achieve a steady profile adaptation pace. We argue that these practices are not attuned to the dynamic nature of user interests. A user may be interested in more than one topic in parallel, and both frequent variations and occasional radical changes of interests are inevitable over time. With our experimental system "Nootropia", we achieve adaptive document filtering with a single, multi-topic user profile. A hierarchical term network that takes into account topical and lexical correlations between terms and identifies topic-subtopic relations between them, is used to represent a user's multiple topics of interest and distinguish between them. A series of non-linear document evaluation functions is then established on the hierarchical network. Experiments using a variation of TREC's routing subtask to test the ability of a single profile to represent two and three topics of interest, reveal the approach's superiority over a linear profile representation. Adaptation of this single, multi-topic profile to a variety of changes in the user interests, is achieved through a process of self-organisation that constantly readjusts the profile stucturally, in response to user feedback. We used virtual users and another variation of TREC's routing subtask to test the profile on two learning and two forgetting tasks. The results clearly indicate the profile's ability to adapt to both frequent variations and radical changes in user interests

Beyond TREC's filtering track

Following the withdrawal of the filtering track from the latest TREC conferences, there is a niche for new evaluation standards. Towards this end, we suggest, based on variations of TREC's routing subtask, two new evaluation methodologies. The first can be used for evaluating single, multi-topic profiles and the second for testing the ability of a multi-topic profile to adapt to both modest variations and radical drifts in user interests

Diagnosis and Treatment of Inappropriate Sinus Tachycardia

Inappropriate sinus tachycardia (IST) is a syndrome of cardiac and extracardiac symptoms characterized by rapid sinus heart rate at rest (>100 bpm) or with minimal activity and disproportionate to the physiologic demands. Patients with this unique and puzzling arrhythmia may require restriction from physical activity. The responsible mechanisms for IST are not completely understood. IST and postural orthostatic tachycardia syndrome (POTS) are the 2 sides of the same coin. It is important to distinguish IST from so-called appropriate sinus tachycardia and from POTS, with which an overlap may occur. As the long-term outcome seems to be benign, treatment may be unnecessary, or may be as simple as physical training. However, for patients with intolerable symptoms, therapeutic measures are warranted. Beta-adrenergic blockers, considered a first-line therapy, are usually ineffective even at high doses; the same applies for most other medical therapies. Ivabradine seems to be more effective than beta-blockers especially in the non- hypertensive patients. In rare instances, catheter- or surgically- based right atrial or sinus node modification may be helpful, but even this is fraught with limited efficacy and potential complications. Overtreatment, in an attempt to reduce symptoms, can be difficult to avoid, but is discouraged. In this report, we will be review IST, explore its mechanisms and evaluate possible management strategies

Sex- and age-related differences in the management and outcomes of chronic heart failure: an analysis of patients from the ESC HFA EORP Heart Failure Long-Term Registry

Aims: This study aimed to assess age- and sex-related differences in management and 1-year risk for all-cause mortality and hospitalization in chronic heart failure (HF) patients. Methods and results: Of 16 354 patients included in the European Society of Cardiology Heart Failure Long-Term Registry, 9428 chronic HF patients were analysed [median age: 66 years; 28.5% women; mean left ventricular ejection fraction (LVEF) 37%]. Rates of use of guideline-directed medical therapy (GDMT) were high (angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, beta-blockers and mineralocorticoid receptor antagonists: 85.7%, 88.7% and 58.8%, respectively). Crude GDMT utilization rates were lower in women than in men (all differences: P\ua0 64 0.001), and GDMT use became lower with ageing in both sexes, at baseline and at 1-year follow-up. Sex was not an independent predictor of GDMT prescription; however, age >75 years was a significant predictor of GDMT underutilization. Rates of all-cause mortality were lower in women than in men (7.1% vs. 8.7%; P\ua0=\ua00.015), as were rates of all-cause hospitalization (21.9% vs. 27.3%; P\ua075 years. Conclusions: There was a decline in GDMT use with advanced age in both sexes. Sex was not an independent predictor of GDMT or adverse outcomes. However, age >75 years independently predicted lower GDMT use and higher all-cause mortality in patients with LVEF 6445%

Multimodal dynamic optimization: From evolutionary algorithms to artificial immune systems

Multimodal Dynamic Optimisation is a challenging problem, used in this paper as a framework for the qualitative comparison between Evolutionary Algorithms and Artificial Immune Systems. It is argued that while Evolutionary Algorithms have inherent diversity problems that do not allow them to successfully deal with multimodal dynamic optimisation, the biological immune system involves natural processes for maintaining and boosting diversity and thus serves well as a metaphor for tackling this problem. We review the basic evolutionary and immune-inspired approaches to multimodal dynamic optimisation, we identify correspondences and differences and point out essential computation elements

Corpus profiling with Nootropia

The characteristics of different corpora influence the success of Information Retrieval and NLP methods. How to best characterise a corpus is still an unexplored research area. In this paper, we use a model that has so far been applied for user profiling in Information Filtering, to profile the corpora of the TIPSTER collection. Each corpus profile is a network of terms that allows the extraction of a series of statistical features. These features can be used to calculate the similarity between the corpora in TIPSTER. This is part of ongoing work that aims at providing a corpus profiling service that will map corpora to their features and to the corresponding experimental results of various models and techniques

Immune-inspired adaptive information filtering

Adaptive information filtering is a challenging research problem. It requires the adaptation of a representation of a user’s multiple interests to various changes in them. We investigate the application of an immune-inspired approach to this problem. Nootropia, is a user profiling model that has many properties in common with computational models of the immune system that have been based on Franscisco Varela’s work. In this paper we concentrate on Nootropia’s evaluation. We define an evaluation methodology that uses virtual user’s to simulate various interest changes. The results show that Nootropia exhibits the desirable adaptive behaviour

A Comparative Evaluation of Term Weighting Methods for Information Filtering

Users of information filtering systems cannot be expected to provide large amounts of information to initialize a profile. Therefore, term weighting methods for information filtering have somewhat different requirements to those for information retrieval and text categorization. We present a comparative evaluation of term weighting methods, including a new method, relative document frequency, designed specifically for information filtering. The best weighting methods appear to be those that favor information provided by the user, over information from a general collection

Nootropia: a user profiling model based on a self-organising term network

Artificial Immune Systems are well suited to the problem of using a profile representation of an individual’s or a group’s interests to evaluate documents. Nootropia is a user profiling model that exhibits similarities to models of the immune system that have been developed in the context of autopoietic theory. It uses a self-organising term network that can represent a user’s multiple interests and can adapt to both shortterm variations and substantial changes in them. This allows Nootropia to drift, constantly following changes in the user’s multiple interests, and, thus, to become structurally coupled to the user