Brain status modeling with non-negative projective dictionary learning

Accurate prediction of individuals’ brain age is critical to establish a baseline for normal brain development. This study proposes to model brain development with a novel non-negative projective dictionary learning (NPDL) approach, which learns a discriminative representation of multi-modal neuroimaging data for predicting brain age. Our approach encodes the variability of subjects in different age groups using separate dictionaries, projecting features into a low-dimensional manifold such that information is preserved only for the corresponding age group. The proposed framework improves upon previous discriminative dictionary learning methods by inc

Defining and Explorting the Intelligence Space

Intelligence is a difficult concept to define, despite many attempts at doing so. Rather than trying to settle on a single definition, this article introduces a broad perspective on what intelligence is, by laying out a cascade of definitions that induces both a nested hierarchy of three levels of intelligence and a wider-ranging space that is built around them and approximations to them. Within this intelligence space, regions are identified that correspond to both natural -- most particularly, human -- intelligence and artificial intelligence (AI), along with the crossover notion of humanlike intelligence. These definitions are then exploited in early explorations of four more advanced, and likely more controversial, topics: the singularity, generative AI, ethics, and intellectual property.Comment: May ultimately appear as a journal article and/or a book chapte

The Incremental Multiresolution Matrix Factorization Algorithm

Multiresolution analysis and matrix factorization are foundational tools in computer vision. In this work, we study the interface between these two distinct topics and obtain techniques to uncover hierarchical block structure in symmetric matrices -- an important aspect in the success of many vision problems. Our new algorithm, the incremental multiresolution matrix factorization, uncovers such structure one feature at a time, and hence scales well to large matrices. We describe how this multiscale analysis goes much farther than what a direct global factorization of the data can identify. We evaluate the efficacy of the resulting factorizations for relative leveraging within regression tasks using medical imaging data. We also use the factorization on representations learned by popular deep networks, providing evidence of their ability to infer semantic relationships even when they are not explicitly trained to do so. We show that this algorithm can be used as an exploratory tool to improve the network architecture, and within numerous other settings in vision.Comment: Computer Vision and Pattern Recognition (CVPR) 2017, 10 page

Exploring a multifactorial, clinical model of thought disorder : application of a dimensional, transdiagnostic approach.

Background: Bleuler saw thought disorder as the core defining feature of psychotic phenomena, reflective of the “splitting of the psychic functions” that occurred when, in the process of thinking, one’s ideas and feelings disconnect, becoming fragmented and competing functions. Unfortunately, interest in thought disorder as the conceptual core of psychosis was lost with rise of the modern DSM system, paralleling the shift towards a more simplistic, categorical way of defining psychiatric disorders. Aims: This study examined thought disorder from a dimensional perspective, with the aim of disentangling qualitative heterogeneity and diverse sources of influence. Analyses were based on a large, transdiagnostic sample (n = 322), including individuals diagnosed with schizophrenia, schizoaffective disorder, and bipolar disorder. Structural equation modeling was used to estimate the unique and combined effects of family psychiatric history, age-at-onset, affective state, and sex on two dimensions of thought disorder, namely idiosyncratic thinking and combinatory thinking. We also explored the utility of categorical (i.e. DSM) diagnosis, by estimating the relative proportion of variance it accounted for within the model. Results: The overall model accounted for 11% of variance in idiosyncratic thinking and 3% of the variance in combinatory thinking. Negative affect was the strongest predictor of idiosyncratic thinking (r = .27), although this effect was significantly more robust in those with a family history of psychosis (r = .37) compared to those without (r = .02). DSM diagnosis was a significant predictor of IV, explaining 7% of unique variance when entered into the full model compared to 9% of the variance when estimated independently, which suggests that the portion of variance explained by diagnosis was largely independent of other predictors in the model. Discussion: The pattern of associations among family psychiatric history, age-at-onset, and negative affect that predicted idiosyncratic thinking are suggestive of a developmental process. This hypothesis is explored in the context of previous research. The broad implications of this research on the classification and study of psychosis is also discussed

Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision

Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language

Imparting 3D representations to artificial intelligence for a full assessment of pressure injuries.

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning methods. The main objective of this dissertation is to prove the efficiency of Deep Learning techniques in tackling one of the important health issues we are facing in our society, through medical imaging. Pressure injuries are a dermatology related health issue associated with increased morbidity and health care costs. Managing pressure injuries appropriately is increasingly important for all the professionals in wound care. Using 2D photographs and 3D meshes of these wounds, collected from collaborating hospitals, our mission is to create intelligent systems for a full non-intrusive assessment of these wounds. Five main tasks have been achieved in this study: a literature review of wound imaging methods using machine learning techniques, the classification and segmentation of the tissue types inside the pressure injury, the segmentation of these wounds and the design of an end-to-end system which measures all the necessary quantitative information from 3D meshes for an efficient assessment of PIs, and the integration of the assessment imaging techniques in a web-based application