Linear machine learning models can be seen as providing two outputs: predictions and weight maps. The latter shows the relative contribution of the individual features to the model and has been heavily used in the neuroimaging community to infer conclusions about brain structure/function. There has however been a recent debate on whether weight maps can provide information about the neural signals leading to a significant classification/regression model [1]-[3]. The authors of [1] indeed suggest that weight maps provide a poor recovery of the input neural signal and lead to false positives. They further demonstrate that the amplitude of the weight does not reflect the amplitude of the signal difference in a feature. However, their examples are specific cases with low signalto-noise ratio (SNR). Here, we investigate the recovery of two widespread techniques, namely SVM [4] and sparse MKL [5] when varying the SNR, as well as the distribution of simulated neural signals.

With the development of deep learning, Deep Metric Learning (DML) has achieved great improvements in face recognition. Specifically, the widely used softmax loss in the training process often bring large intra-class variations, and feature normalization is only exploited in the testing process to compute the pair similarities. To bridge the gap, we impose the intra-class cosine similarity between the features and weight vectors in softmax loss larger than a margin in the training step, and extend it from four aspects. First, we explore the effect of a hard sample mining strategy. To alleviate the human labor of adjusting the margin hyper-parameter, a self-adaptive margin updating strategy is proposed. Then, a normalized version is given to take full advantage of the cosine similarity constraint. Furthermore, we enhance the former constraint to force the intra-class cosine similarity larger than the mean inter-class cosine similarity with a margin in the exponential feature projection space. Extensive experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and IARPA Janus Benchmark A (IJB-A) datasets demonstrate that the proposed methods outperform the mainstream DML methods and approach the state-of-the-art performance.

Motivation: Ontologies are widely used in biology for data annotation, integration, and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotations commonly used in ontologies include class labels, descriptions, or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally unexploited by ontology-based analysis methods such as semantic similarity measures. Results: We propose a novel method, OPA2Vec, to generate vector representations of biological entities in ontologies by combining formal ontology axioms and annotation axioms from the ontology meta-data. We apply a Word2Vec model that has been pre-trained on PubMed abstracts to produce feature vectors from our collected data. We validate our method in two different ways: first, we use the obtained vector representations of proteins as a similarity measure to predict protein-protein interaction (PPI) on two different datasets. Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations. These two experiments are just an illustration of the possible applications of our method. OPA2Vec can be used to produce vector representations of any biomedical entity given any type of biomedical ontology. Availability: https://github.com/bio-ontology-research-group/opa2vec Contact: robert.hoehndorf@kaust.edu.sa and xin.gao@kaust.edu.sa.

Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets.

Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properties. The growing availability of new types of drug features brings the opportunity of learning a more comprehensive and accurate drug similarity that represents the full spectrum of underlying drug relations. However, it is challenging to integrate these heterogeneous, noisy, nonlinear-related information to learn accurate similarity measures especially when labels are scarce. Moreover, there is a trade-off between accuracy and interpretability. In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features. In particular, we model the integration using multi-view graph auto-encoders, and add attentive mechanism to determine the weights for each view with respect to corresponding tasks and features for better interpretability. Our model has flexible design for both semi-supervised and unsupervised settings. Experimental results demonstrated significant predictive accuracy improvement. Case studies also showed better model capacity (e.g. embed node features) and interpretability.

Machine learning and data science require more than just throwing data into a python library and utilizing whatever comes out. Data scientists need to actually understand the data and the processes behind the data to be able to implement a successful system. One key methodology to implementation is knowing when a model might benefit from utilizing bootstrapping methods. These are what are called ensemble models. Some examples of ensemble models are AdaBoost and Stochastic Gradient Boosting. They can help improve algorithm accuracy or improve the robustness of a model.

ROC is a graphic plot illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The critical point here is "binary classifier" and "varying threshold". I will show you how to plot ROC for multi-label classifier by the one-vs-all approach as well. AUC is the percentage of this area that is under this ROC curve, ranging between 0 1. ROC is a great way to visualize the performance of a binary classifier, and AUC is one single number to summarize a classifier's performance by assessing the ranking regarding separation of the two classes.

Smart machines are giving storytellers and risk managers alike a helping hand. Burgeoning data analyzed by ever more intelligent machines are opening pathways to surprising applications and providing solutions to problems that have been out of reach. In the film industry, machines "watch" movies and videos, charting their emotional intensity and giving content creators clues about to how to make stories more appealing. And in banking, AI's ability to detect anomalies among millions of transactions helps bank risk officers eliminate false positives that are a drain on productivity. For a growing number of industries, AI is tilting the playing field--you'll need to understand how before your competitors do. Machine-learning models can help screenwriters and directors fine-tune scripts and imagery.

A startup that uses artificial intelligence to help emergency dispatchers identify signs of cardiac arrest over the phone will begin testing its software across Europe this summer. Danish firm Corti says its algorithms can recognize out-of-hospital cardiac arrests (those that occur in the home or public) more quickly and accurately than humans. The software has already been deployed in Copenhagen, but this year, it will start four new pilots in as-yet-unnamed European cities in partnership with the European Emergency Number Association (EENA). Quick recognition of cardiac arrests is vital, as every minute that passes without treatment reduces an individual's chances of survival by 7 to 10 percent. Corti's software works by listening in during emergency calls and looking out for a number of "verbal and non-verbal patterns of communication." These include cues like a caller's tone of voice and whether or not the subject is breathing.

Every year, thousands of people receive consumer product related injuries. Research indicates that online customer reviews can be processed to autonomously identify product safety issues. Early identification of safety issues can lead to earlier recalls, and thus fewer injuries and deaths. A dataset of product reviews from Amazon.com was compiled, along with \emph{SaferProducts.gov} complaints and recall descriptions from the Consumer Product Safety Commission (CPSC) and European Commission Rapid Alert system. A system was built to clean the collected text and to extract relevant features. Dimensionality reduction was performed by computing feature relevance through a Random Forest and discarding features with low information gain. Various classifiers were analyzed, including Logistic Regression, SVMs, Na{\"i}ve-Bayes, Random Forests, and an Ensemble classifier. Experimentation with various features and classifier combinations resulted in a logistic regression model with 70.2\% precision in the top 50 reviews surfaced. This classifier outperforms all benchmarks set by related literature and consumer product safety professionals.