Explore the vast range of titles available.

Video Question Answering (Video QA) challenges modelers in multiple fronts. Modeling video necessitates building not only spatio-temporal models for the dynamic visual channel but also multimodal structures for associated information channels such as subtitles or audio. Video QA adds at least two more layers of complexity – selecting relevant content for each channel in the context of the linguistic query, and composing spatio-temporal concepts and relations hidden in the data in response to the query. To address these requirements, we start with two insights: (a) content selection and relation construction can be jointly encapsulated into a conditional computational structure, and (b) video-length structures can be composed hierarchically. For (a) this paper introduces a general-reusable reusable neural unit dubbed Conditional Relation Network (CRN) taking as input a set of tensorial objects and translating into a new set of objects that encode relations of the inputs. The generic design of CRN helps ease the common complex model building process of Video QA by simple block stacking and rearrangements with flexibility in accommodating diverse input modalities and conditioning features across both visual and linguistic domains. As a result, we realize insight (b) by introducing Hierarchical Conditional Relation Networks (HCRN) for Video QA. The HCRN primarily aims at exploiting intrinsic properties of the visual content of a video as well as its accompanying channels in terms of compositionality, hierarchy, and near-term and far-term relation. HCRN is then applied for Video QA in two forms, short-form where answers are reasoned solely from the visual content of a video, and long-form where an additional associated information channel, such as movie subtitles, presented. Our rigorous evaluations show consistent improvements over state-of-the-art methods on well-studied benchmarks including large-scale real-world datasets such as TGIF-QA and TVQA, demonstrating the strong capabilities of our CRN unit and the HCRN for complex domains such as Video QA. To the best of our knowledge, the HCRN is the very first method attempting to handle long and short-form multimodal Video QA at the same time.

Background Technological advances in next-generation sequencing (NGS) and chromatographic assays [e.g., liquid chromatography mass spectrometry (LC-MS)] have made it possible to identify thousands of microbe and metabolite species, and to measure their relative abundance. In this paper, we propose a sparse neural encoder-decoder network to predict metabolite abundances from microbe abundances. Results Using paired data from a cohort of inflammatory bowel disease (IBD) patients, we show that our neural encoder-decoder model outperforms linear univariate and multivariate methods in terms of accuracy, sparsity, and stability. Importantly, we show that our neural encoder-decoder model is not simply a black box designed to maximize predictive accuracy. Rather, the network’s hidden layer (i.e., the latent space, comprised only of sparsely weighted microbe counts) actually captures key microbe-metabolite relationships that are themselves clinically meaningful. Although this hidden layer is learned without any knowledge of the patient’s diagnosis, we show that the learned latent features are structured in a way that predicts IBD and treatment status with high accuracy. Conclusions By imposing a non-negative weights constraint, the network becomes a directed graph where each downstream node is interpretable as the additive combination of the upstream nodes. Here, the middle layer comprises distinct microbe-metabolite axes that relate key microbial biomarkers with metabolite biomarkers. By pre-processing the microbiome and metabolome data using compositional data analysis methods, we ensure that our proposed multi-omics workflow will generalize to any pair of -omics data. To the best of our knowledge, this work is the first application of neural encoder-decoders for the interpretable integration of multi-omics biological data.

Background In the age of big data, linked social and administrative health data in combination with machine learning (ML) is being increasingly used to improve prediction in chronic disease, e.g., cardiovascular diseases (CVD). In this study we aimed to apply ML methods on extensive national-level health and social administrative datasets to assess the utility of these for predicting future diabetes complications, including by ethnicity. Methods Five ML models were used to predict CVD events among all people with known diabetes in the population of New Zealand, utilizing nationwide individual-level administrative data. Results The Xgboost ML model had the best predictive power for predicting CVD events three years into the future among the population with diabetes ( N  = 145,600). The optimization procedure also found limited improvement in prediction by ethnicity (using area under the receiver operating curve, [AUC]). The results indicated no trade-off between model predictive performance and equity gap of prediction by ethnicity (that is improving model prediction and reducing performance gaps by ethnicity can be achieved simultaneously). The list of variables of importance was different among different models/ethnic groups, for example: age, deprivation (neighborhood-level), having had a hospitalization event, and the number of years living with diabetes. Discussion and conclusions We provide further evidence that ML with administrative health data can be used for meaningful future prediction of health outcomes. As such, it could be utilized to inform health planning and healthcare resource allocation for diabetes management and the prevention of CVD events. Our results may suggest limited scope for developing prediction models by ethnic group and that the major ways to reduce inequitable health outcomes is probably via improved delivery of prevention and management to those groups with diabetes at highest need.

A preference relation -based Top-N recommendation approach is proposed to capture both second-order and higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings first, and then inferring the item rankings, based on the assumption of availability of explicit feedback such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed approach drops these assumptions by exploiting preference relations , a more practical user feedback. Furthermore, the proposed approach enjoys the representational power of Markov Random Fields thus side information such as item and user attributes can be easily incorporated. Comparing to related work, the proposed approach has the unique property of modeling both second-order and higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference-relation based methods. Experimental results on public datasets demonstrate that both types of interactions have been properly captured, and significantly improved Top-N recommendation performance has been achieved.

We address a largely open problem of multilabel classification over graphs. Unlike traditional vector input, a graph has rich variable-size substructures which are related to the labels in some ways. We believe that uncovering these relations might hold the key to classification performance and explainability. We introduce Graph Attention model for Multi-Label learning (\\[\\text {GAML}\\]), a novel graph neural network that can handle this problem effectively. \\[\\text {GAML}\\] regards labels as auxiliary nodes and models them in conjunction with the input graph. By applying the neural message passing algorithm and attention mechanism to both the label nodes and the input nodes iteratively, \\[\\text {GAML}\\] can capture the relations between the labels and the input subgraphs at various resolution scales. Moreover, our model can take advantage of explicit label dependencies. It also scales linearly with the number of labels and graph size thanks to our proposed hierarchical attention. We evaluate \\[\\text {GAML}\\] on an extensive set of experiments with both graph-structured inputs and classical unstructured inputs. The results show that \\[\\text {GAML}\\] significantly outperforms other competing methods. Importantly, \\[\\text {GAML}\\] enables intuitive visualizations for better understanding of the label-substructure relations and explanation of the model behaviors.

Background To date, our ability to accurately identify patients at high risk from suicidal behaviour, and thus to target interventions, has been fairly limited. This study examined a large pool of factors that are potentially associated with suicide risk from the comprehensive electronic medical record (EMR) and to derive a predictive model for 1–6 month risk. Methods 7,399 patients undergoing suicide risk assessment were followed up for 180 days. The dataset was divided into a derivation and validation cohorts of 4,911 and 2,488 respectively. Clinicians used an 18-point checklist of known risk factors to divide patients into low, medium, or high risk. Their predictive ability was compared with a risk stratification model derived from the EMR data. The model was based on the continuation-ratio ordinal regression method coupled with lasso (which stands for least absolute shrinkage and selection operator). Results In the year prior to suicide assessment, 66.8% of patients attended the emergency department (ED) and 41.8% had at least one hospital admission. Administrative and demographic data, along with information on prior self-harm episodes, as well as mental and physical health diagnoses were predictive of high-risk suicidal behaviour. Clinicians using the 18-point checklist were relatively poor in predicting patients at high-risk in 3 months (AUC 0.58, 95% CIs: 0.50 – 0.66). The model derived EMR was superior (AUC 0.79, 95% CIs: 0.72 – 0.84). At specificity of 0.72 (95% CIs: 0.70-0.73) the EMR model had sensitivity of 0.70 (95% CIs: 0.56-0.83). Conclusion Predictive models applied to data from the EMR could improve risk stratification of patients presenting with potential suicidal behaviour. The predictive factors include known risks for suicide, but also other information relating to general health and health service utilisation.

Anomalies are those deviating significantly from the norm. Thus, anomaly detection amounts to finding data points located far away from their neighbors, i.e., those lying in low-density regions. Classic anomaly detection methods are largely designed for single data type such as continuous or discrete. However, real-world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Mixed data poses multiple challenges including (a) capturing the inter-type correlation structures and (b) measuring deviation from the norm under multiple types. These challenges are exaggerated under (c) high-dimensional regimes. In this paper, we propose a new scalable unsupervised anomaly detection method for mixed data based on Mixed-variate Restricted Boltzmann Machine (Mv.RBM). The Mv.RBM is a principled probabilistic method that estimates density of mixed data. We propose to use free energy derived from Mv.RBM as anomaly score as it is identical to data negative log-density up to an additive constant. We then extend this method to detect anomalies across multiple levels of data abstraction, an effective approach to deal with high-dimensional settings. The extension is dubbed \\[\\mathtt {MIXMAD}\\], which stands for MIXed data Multilevel Anomaly Detection. In \\[\\mathtt {MIXMAD}\\], we sequentially construct an ensemble of mixed-data Deep Belief Nets (DBNs) with varying depths. Each DBN is an energy-based detector at a predefined abstraction level. Predictions across the ensemble are finally combined via a simple rank aggregation method. The proposed methods are evaluated on a comprehensive suit of synthetic and real high-dimensional datasets. The results demonstrate that for anomaly detection, (a) a proper handling of mixed types is necessary, (b) free energy is a powerful anomaly scoring method, (c) multilevel abstraction of data is important for high-dimensional data, and (d) empirically Mv.RBM and \\[\\mathtt {MIXMAD}\\] are superior to popular unsupervised detection methods for both homogeneous and mixed data.

Today’s software development is typically driven by incremental changes made to software to implement a new functionality, fix a bug, or improve its performance and security. Each change request is often described as an issue. Recent studies suggest that a set of components (e.g., software modules) relevant to the resolution of an issue is one of the most important information provided with the issue that software engineers often rely on. However, assigning an issue to the correct component(s) is challenging, especially for large-scale projects which have up to hundreds of components. In this paper, we propose a predictive model which learns from historical issue reports and recommends the most relevant components for new issues. Our model uses Long Short-Term Memory, a deep learning technique, to automatically learn semantic features representing an issue report, and combines them with the traditional textual similarity features. An extensive evaluation on 142,025 issues from 11 large projects shows that our approach outperforms one common baseline, two state-of-the-art techniques, and six alternative techniques with an improvement of 16.70%–66.31% on average across all projects in predictive performance.

Precision psychiatry is attracting increasing attention lately as a recognized priority. One of the goals of precision psychiatry is to develop tools capable of aiding a clinically informed psychiatric diagnosis objectively. Cognitive, inflammatory and immunological factors are altered in both bipolar disorder (BD) and schizophrenia (SZ), however, most of these alterations do not respect diagnostic boundaries from a phenomenological perspective and possess great variability in different individuals with the same phenotypic diagnosis and, consequently, none so far has proven to have the ability of reliably aiding in the differential diagnosis of BD and SZ. We developed a probabilistic multi-domain data integration model consisting of immune and inflammatory biomarkers in peripheral blood and cognitive biomarkers using machine learning to predict diagnosis of BD and SZ. A total of 416 participants, being 323, 372, and 279 subjects for blood, cognition and combined biomarkers analysis, respectively. Our multi-domain model performances for the BD vs. control (sensitivity 80% and specificity 71%) and for the SZ vs. control (sensitivity 84% and specificity 81%) pairs were high in general, however, our multi-domain model had only moderate performance for the differential diagnosis of BD and SZ (sensitivity 71% and specificity 73%). In conclusion, our results show that the diagnosis of BD and of SZ, and that the differential diagnosis of BD and SZ can be predicted with possible clinical utility by a computational machine learning algorithm employing blood and cognitive biomarkers, and that their integration in a multi-domain outperforms algorithms based in only one domain. Independent studies are needed to validate these findings.