Motivation: Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations makes them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications. Results: We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. To evaluate Onto2Vec, we use the Gene Ontology (GO) and jointly produce dense vector representations of proteins, the GO classes to which they are annotated, and the axioms in GO that constrain these classes.

Telling cause from effect using observational data is a challenging problem, especially in the bivariate case. Contemporary methods often assume an independence between the cause and the generating mechanism of the effect given the cause. From this postulate, they derive asymmetries to uncover causal relationships. In this work, we propose such an approach, based on the link between Kolmogorov complexity and quantile scoring. We use a nonparametric conditional quantile estimator based on copulas to implement our procedure, thus avoiding restrictive assumptions about the joint distribution between cause and effect. In an extensive study on real and synthetic data, we show that quantile copula causal discovery (QCCD) compares favorably to state-of-the-art methods, while at the same time being computationally efficient and scalable.

--We study the problem of learning to rank from multiple sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a composite ranking method while keeping a close correlation with the individual rankings simultaneously . We propose a multi-objective solution to ranking by capturing the information of the feature mapping from both within each view as well as across views using autoencoder-like networks. Moreover, a novel end-to-end solution is introduced to enhance the joint ranking with minimum view-specific ranking loss, so that we can achieve the maximum global view agreements within a single optimization process. The proposed method is validated on a wide variety of ranking problems, including university ranking, multi-view lingual text ranking and image data ranking, providing superior results. Learning to rank is an important research topic in information retrieval and data mining, which aims to learn a ranking model to produce a query-specfic ranking list. The ranking model establishes a relationship between each pair of data samples by combining the corresponding features in an optimal way [1]. A score is then assigned to each pair to evaluate its relevance forming a global ranking list across all pairs. The success of learning to rank solutions has brought a wide spectrum of applications, including online advertising [2], natural language processing [3] and multimedia retrieval [4]. Learning appropriate data representation and a suitable scoring function are two vital steps in the ranking problem. T raditionally, a feature mapping models the data distribution in a latent space to match the relevance relationship, while the scoring function is used to quantify the relevance measure [1]; however, the ranking problem in the real world emerges from multiple facets and data patterns are mined from diverse domains.

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately.

Receiver operating characteristic (ROC) analysis is widely used for evaluating diagnostic systems. Recent studies have shown that estimating an area under ROC curve (AUC) with standard cross-validation methods suffers from a large bias. The leave-pair-out (LPO) cross-validation has been shown to correct this bias. However, while LPO produces an almost unbiased estimate of AUC, it does not provide a ranking of the data needed for plotting and analyzing the ROC curve. In this study, we propose a new method called tournament leave-pair-out (TLPO) cross-validation. This method extends LPO by creating a tournament from pair comparisons to produce a ranking for the data. TLPO preserves the advantage of LPO for estimating AUC, while it also allows performing ROC analysis. We have shown using both synthetic and real world data that TLPO is as reliable as LPO for AUC estimation and confirmed the bias in leave-one-out cross-validation on low-dimensional data.

People sending final goodbye messages to their loved ones. Mother clinging to children, while the father hurriedly makes for a safe getaway for the family to the basement. Imagine the state of confusion and chaos every Hawaiian would have faced on Jan 13, 2018 when they received an approaching ballistic missile alert. Well given the current twitter threats by'Rocket Man' as President Trump calls the North Korean dictator, many Hawaiian's had all the logical reasons to believe the alert. Thankfully'Rocket Man' had not pressed the tiny nuclear button probably because Trump had a bigger button!! It all thankfully turned out to be a'False Alarm'.

People sending final goodbye messages to their loved ones. Mother clinging to children, while the father hurriedly makes for a safe getaway for the family to the basement. Imagine the state of confusion and chaos every Hawaiian would have faced on Jan 13, 2018 when they received an approaching ballistic missile alert. Well given the current twitter threats by'Rocket Man' as President Trump calls the North Korean dictator, many Hawaiian's had all the logical reasons to believe the alert. Thankfully'Rocket Man' had not pressed the tiny nuclear button probably because Trump had a bigger button!! It all thankfully turned out to be a'False Alarm'.

The first WWE pay-per-view is also among the year's biggest. Royal Rumble 2018 kicks off the road to WrestleMania Sunday night in Philadelphia to determine two of the wrestlers that will challenge for titles in New Orleans on April 8. As one of the "Big Four" pay-per-views, the Royal Rumble begins earlier than usual with a 7 p.m. EST start time. The kickoff show starts at 5 p.m. EST on WWE Network, which costs subscribers $9.99 per month, followed by the Royal Rumble itself. Fans can watch the Royal Rumble on PPV for $54.99.

While we commonly associate recommendation systems with e-commerce, their application extends to any decision-making problem which requires pairing two types of things together. To understand why recommenders don't always work as well as we'd like them to, we set out to build some basic recommendation systems using publicly available data. The first ingredient for building a recommendation system is user interaction data. We experimented with two different datasets, one from Flickr and one from Amazon. The Flickr dataset contains interactions between users and photos that they liked; the Amazon dataset contains user ratings on books.

Internet of things (IoT) applications have become increasingly popular in recent years, with applications ranging from building energy monitoring to personal health tracking and activity recognition. In order to leverage these data, automatic knowledge extraction - whereby we map from observations to interpretable states and transitions - must be done at scale. As such, we have seen many recent IoT data sets include annotations with a human expert specifying states, recorded as a set of boundaries and associated labels in a data sequence. These data can be used to build automatic labeling algorithms that produce labels as an expert would. Here, we refer to human-specified boundaries as breakpoints. Traditional changepoint detection methods only look for statistically-detectable boundaries that are defined as abrupt variations in the generative parameters of a data sequence. However, we observe that breakpoints occur on more subtle boundaries that are non-trivial to detect with these statistical methods. In this work, we propose a new unsupervised approach, based on deep learning, that outperforms existing techniques and learns the more subtle, breakpoint boundaries with a high accuracy. Through extensive experiments on various real-world data sets - including human-activity sensing data, speech signals, and electroencephalogram (EEG) activity traces - we demonstrate the effectiveness of our algorithm for practical applications. Furthermore, we show that our approach achieves significantly better performance than previous methods.