Semantic WordRank
We present Semantic WordRank (SWR), an unsupervised method for generating an extractive summary of a single document. Built on a weighted word graph with semantic and co-occurrence edges, SWR scores sentences using an article-structure-biased PageRank algorithm with a Softplus function adjustment, and promotes topic diversity using spectral subtopic clustering under the Word-Movers-Distance metric. We evaluate SWR on the DUC-02 and SummBank datasets and show that SWR produces better summaries than the state-of-the-art algorithms over DUC-02 under common ROUGE measures. We then show that, under the same measures over SummBank, SWR outperforms each of the three human annotators (aka. judges) and compares favorably with the combined performance of all judges. …

VelocityGAN
Acoustic- and elastic-waveform inversion is an important and widely used method to reconstruct subsurface velocity image. Waveform inversion is a typical non-linear and ill-posed inverse problem. Existing physics-driven computational methods for solving waveform inversion suffer from the cycle skipping and local minima issues, and not to mention solving waveform inversion is computationally expensive. In this paper, we developed a real-time data-driven technique, VelocityGAN, to accurately reconstruct subsurface velocities. Our VelocityGAN is an end-to-end framework which can generate high-quality velocity images directly from the raw seismic waveform data. A series of numerical experiments are conducted on the synthetic seismic reflection data to evaluate the effectiveness and efficiency of VelocityGAN. We not only compare it with existing physics-driven approaches but also choose some deep learning frameworks as our data-driven baselines. The experiment results show that VelocityGAN outperforms the physics-driven waveform inversion methods and achieves the state-of-the-art performance among data-driven baselines. …

Adaptive Linear Neuron (ADALINE)
ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network. The network uses memistors. It was developed by Professor Bernard Widrow and his graduate student Ted Hoff at Stanford University in 1960. It is based on the McCulloch-Pitts neuron. It consists of a weight, a bias and a summation function. The difference between Adaline and the standard (McCulloch-Pitts) perceptron is that in the learning phase the weights are adjusted according to the weighted sum of the inputs (the net). In the standard perceptron, the net is passed to the activation (transfer) function and the function’s output is used for adjusting the weights. There also exists an extension known as Madaline. …

Pathway Induced Multiple Kernel Learning (PIMKL)
Reliable identification of molecular biomarkers is essential for accurate patient stratification. While state-of-the-art machine learning approaches for sample classification continue to push boundaries in terms of performance, most of these methods are not able to integrate different data types and lack generalization power limiting their application in a clinical setting. Furthermore, many methods behave as black boxes, therefore we have very little understanding about the mechanisms that lead to the prediction provided. While opaqueness concerning machine behaviour might not be a problem in deterministic domains, in health care, providing explanations about the molecular factors and phenotypes that are driving the classification is crucial to build trust in the performance of the predictive system. We propose Pathway Induced Multiple Kernel Learning (PIMKL), a novel methodology to classify samples reliably that can, at the same time, provide a pathway-based molecular fingerprint of the signature that underlies the classification. PIMKL exploits prior knowledge in the form of molecular interaction networks and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning algorithm (MKL), an approach that has demonstrated excellent performance in different machine learning applications. After optimizing the combination of kernels for prediction of a specific phenotype, the model provides a stable molecular signature that can be interpreted in the light of the ingested prior knowledge and that can be used in transfer learning tasks. …