Pareto-Path Multi-Task Multiple Kernel Learning

A traditional and intuitively appealing Multi-Task Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing amongst tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a Multi-Objective Optimization (MOO) problem, which considers the concurrent optimization of all task objectives involved in the Multi-Task Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel Support Vector Machine (SVM) MT-MKL framework, that considers an implicitly-defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving better classification performance, when compared to other similar MTL approaches.Comment: Accepted by IEEE Transactions on Neural Networks and Learning System

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

International Committee on Mental Health in Cystic Fibrosis: Cystic Fibrosis Foundation and European Cystic Fibrosis Society consensus statements for screening and treating depression and anxiety

Studies measuring psychological distress in individuals with cystic fibrosis (CF) have found high rates of both depression and anxiety. Psychological symptoms in both individuals with CF and parent caregivers have been associated with decreased lung function, lower body mass index, worse adherence, worse health-related quality of life, more frequent hospitalisations and increased healthcare costs. To identify and treat depression and anxiety in CF, the CF Foundation and the European CF Society invited a panel of experts, including physicians, psychologists, psychiatrists, nurses, social workers, a pharmacist, parents and an individual with CF, to develop consensus recommendations for clinical care. Over 18 months, this 22-member committee was divided into four workgroups: Screening; Psychological Interventions; Pharmacological Treatments and Implementation and Future Research, and used the Population, Intervention, Comparison, Outcome methodology to develop questions for literature search and review. Searches were conducted in PubMed, PsychINFO, ScienceDirect, Google Scholar, Psychiatry online and ABDATA by a methodologist at Dartmouth. The committee reviewed 344 articles, drafted statements and set an 80% acceptance for each recommendation statement as a consensus threshold prior to an anonymous voting process. Fifteen guideline recommendation statements for screening and treatment of depression and anxiety in individuals with CF and parent caregivers were finalised by vote. As these recommendations are implemented in CF centres internationally, the process of dissemination, implementation and resource provision should be closely monitored to assess barriers and concerns, validity and use

Reduced-Rank Local Distance Metric Learning

Abstract. We propose a new method for local metric learning based on a conical combination of Mahalanobis metrics and pair-wise similarities between the data. Its formulation allows for controlling the rank of the metrics ’ weight matrices. We also offer a convergent algorithm for training the associated model. Experimental results on a collection of classification problems imply that the new method may offer notable performance advantages over alternative metric learning approaches that have recently appeared in the literature

Cystic Fibrosis Foundation and European Cystic Fibrosis Society Survey of cystic fibrosis mental health care delivery

Background: Psychological morbidity in individuals with cystic fibrosis (CF) and their caregivers is common. The Cystic Fibrosis Foundation (CFF) and European Cystic Fibrosis Society (ECFS) Guidelines Committee on Mental Health sought the views of CF health care professionals concerning mental health care delivery. Methods: An online survey which focused on the current provision and barriers to mental health care was distributed to CF health care professionals. Results: Of the 1454 respondents, many did not have a colleague trained in mental health issues and 20% had no one on their team whose primary role was focused on assessing or treating these issues. Insufficient resources and a lack of competency were reported in relation to mental health referrals. Seventy-three percent of respondents had no experience with mental health screening. Of those who did, they utilized 48 different, validated scales. Conclusions: These data have informed the decision-making, dissemination and implementation strategies of the Mental Health Guidelines Committee sponsored by the CFF and ECFS

Recognition and management of perinatal depression and anxiety by general practitioners: a systematic review

Background. Perinatal anxiety and depression are widespread, with up to 20% of women affected during pregnancy and after birth. In the UK, management of perinatal mental health falls under the remit of general practitioners (GPs). We reviewed the literature on GPs’ routine recognition, diagnosis and management of anxiety and depression in the perinatal period. Method. A systematic search of Embase, Medline, PsycInfo, Pubmed, Scopus and Web of Science was conducted. Studies were eligible if they reported quantitative measures of GPs’ or Family Physicians’ assessment, recognition and management of anxiety or depression in pregnancy or post-partum. Results. Thirteen papers, reporting 10 studies, were identified from the United States, Australia, UK, Netherlands and Canada. All reported on depression; two included anxiety disorders. Reported awareness and ability to diagnose perinatal depression among GPs was high. GPs knew about and used screening tools in the UK but less so in US settings. Antidepressants were the first line of treatment, with various SSRIs considered safest. Counseling by GPs and referrals to specialists were common in the post-natal period, less so in pregnancy. Treatment choices were determined by resources, attitudes, knowledge and training. Conclusions. Data on GPs’ awareness and management of perinatal depression were sparse and unlikely to be generalizable. Future directions for research are proposed; such as exploring the management of anxiety disorders which are largely missing from the literature, and understanding more about barriers to disclosure and recognition in primary care. More standardized training could help to improve recognition and management practices

Literature Review For Networking And Communication Technology

Report documents the results of a literature search performed in the area of networking and communication technology