Multi-View Interactive Collaborative Filtering

In many scenarios, recommender system user interaction data such as clicks or ratings is sparse, and item turnover rates (e.g., new articles, job postings) high. Given this, the integration of contextual "side" information in addition to user-item ratings is highly desirable. Whilst there are algorithms that can handle both rating and contextual data simultaneously, these algorithms are typically limited to making only in-sample recommendations, suffer from the curse of dimensionality, and do not incorporate multi-armed bandit (MAB) policies for long-term cumulative reward optimization. We propose multi-view interactive topic regression (MV-ICTR) a novel partially online latent factor recommender algorithm that incorporates both rating and contextual information to model item-specific feature dependencies and users' personal preferences simultaneously, with multi-armed bandit policies for continued online personalization. The result is significantly increased performance on datasets with high percentages of cold-start users and items.Comment: Submitted to NeurIPS 2023. Under revie

Schizotypy, lifestyle behaviors, and health indicators in a young adult sample

Problematic lifestyle behaviors and high rates of physical illness are well documented in people with schizophrenia, contributing to premature mortality. Yet, there is a notable absence of research examining general lifestyle and health issues in participants at risk for psychosis. This form of research may help identify concerns that exist during prodromal periods related to future outcomes. Accordingly, the current study examined lifestyle and health in a nonclinical sample of 530 young adults with varying levels of schizotypy. Increasing symptom severity was associated with greater somatic symptoms and poorer sleep quality across positive, negative, and disorganized domains. Elevated negative and disorganized symptoms were associated with significantly reduced health-related quality of life, while evidence for reduced engagement in health behaviors was largely limited to those with elevated negative schizotypy. No relationships emerged between symptom presentation/severity and body mass index or substance use, although zero-order correlations suggested an association between disorganized schizotypy and nicotine use. The pattern of relationships in the current study was consistent with findings from the ultra-high risk and clinical literature suggesting that lifestyle and health concerns may exist on a continuum with psychosis. Future research should seek to clarify if these patterns are associated with long-term physical or mental health outcomes

Board # 29 : A PATTERN RECOGNITION APPROACH TO SIGNAL TO NOISE RATIO ESTIMATION OF SPEECH

A blind approach for estimating the signal to noise ratio (SNR) of a speech signal corrupted by additive noise is proposed. The method is based on a pattern recognition paradigm using various linear predictive based features, a vector quantizer classifier and estimation combination. Blind SNR estimation is very useful in biometric speaker identification systems in which a confidence metric is determined along with the speaker identity. The confidence metric is partially based on the mismatch between the training and testing conditions of the speaker identification system and SNR estimation is very important in evaluating the degree of this mismatch. The educational impact of this project is two-fold: 1. Undergraduate students are initiated into research/development by working on a team to achieve a software implementation of the SNR estimation system. The students will also evaluate the performance of the system by experimenting with different features and classifiers. Producing a paper in a refereed technical conference is the objective. 2. The students will also write a laboratory manual for a portion of this project to be run in a junior level signals and systems class and a senior level class on speech processing. Producing a paper in a refereed education conference is the objective. The learning outcomes for the students engaged in research and for the students doing the project in a class include: • Enhanced application of math skills • Enhanced software implementation skills • Enhanced interest in signal processing • Enhanced ability to analyze experimental results • Enhanced communication skills. The assessment instruments include: • Student surveys (target versus control group comparison that includes a statistical analysis) • Faculty tracking of student learning outcomes based on student work • Faculty evaluation of student work based on significant rubrics • A concept inventory tes

Autoantibodies as Diagnostic Biomarkers for the Detection and Subtyping of Multiple Sclerosis

The goal of this preliminary proof-of-concept study was to use human protein microarrays to identify blood-based autoantibody biomarkers capable of diagnosing multiple sclerosis (MS). Using sera from 112 subjects, including 51 MS subjects, autoantibody biomarkers effectively differentiated MS subjects from age- and gender-matched normal and breast cancer controls with 95.0% and 100% overall accuracy, but not from subjects with Parkinson\u27s disease. Autoantibody biomarkers were also useful in distinguishing subjects with the relapsing-remitting form of MS from those with the secondary progressive subtype. These results demonstrate that autoantibodies can be used as noninvasive blood-based biomarkers for the detection and subtyping of M

Detection of Alzheimer\u27s disease at mild cognitive impairment and disease progression using autoantibodies as blood-based biomarkers

Introduction There is an urgent need to identify biomarkers that can accurately detect and diagnose Alzheimer\u27s disease (AD). Autoantibodies are abundant and ubiquitous in human sera and have been previously demonstrated as disease-specific biomarkers capable of accurately diagnosing mild-moderate stages of AD and Parkinson\u27s disease. Methods Sera from 236 subjects, including 50 mild cognitive impairment (MCI) subjects with confirmed low CSF Aβ42 levels, were screened with human protein microarrays to identify potential biomarkers for MCI. Autoantibody biomarker performance was evaluated using Random Forest and Receiver Operating Characteristic curves. Results Autoantibody biomarkers can differentiate MCI patients from age-matched and gender-matched controls with an overall accuracy, sensitivity, and specificity of 100.0%. They were also capable of differentiating MCI patients from those with mild-moderate AD and other neurologic and non-neurologic controls with high accuracy. Discussion Autoantibodies can be used as noninvasive and effective blood-based biomarkers for early diagnosis and staging of AD

Potential utility of autoantibodies as blood-based biomarkers for early detection and diagnosis of Parkinson’s disease

Introduction There is a great need to identify readily accessible, blood-based biomarkers for Parkinson’s disease (PD) that are useful for accurate early detection and diagnosis. This advancement would allow early patient treatment and enrollment into clinical trials, both of which would greatly facilitate the development of new therapies for PD. Methods Sera from a total of 398 subjects, including 103 early-stage PD subjects derived from the Deprenyl and Tocopherol Antioxidative Therapy of Parkinsonism (DATATOP) study, were screened with human protein microarrays containing 9,486 potential antigen targets to identify autoantibodies potentially useful as biomarkers for PD. A panel of selected autoantibodies with a higher prevalence in early-stage PD was identified and tested using Random Forest for its ability to distinguish early-stage PD subjects from controls and from individuals with other neurodegenerative and non-neurodegenerative diseases. Results Results demonstrate that a panel of selected, blood-borne autoantibody biomarkers can distinguish early-stage PD subjects (90% confidence in diagnosis) from age- and sex-matched controls with an overall accuracy of 87.9%, a sensitivity of 94.1% and specificity of 85.5%. These biomarkers were also capable of differentiating patients with early-stage PD from those with more advanced (mild-moderate) PD with an overall accuracy of 97.5%, and could distinguish subjects with early-stage PD from those with other neurological (e.g., Alzheimer’s disease and multiple sclerosis) and non-neurological (e.g., breast cancer) diseases. Conclusion These results demonstrate, for the first time, that a panel of selected autoantibodies may prove to be useful as effective blood-based biomarkers for the diagnosis of early-stage PD