Supervised phrase-boundary embeddings

We propose a new word embedding model, called SPhrase, that incorporates supervised phrase information. Our method modifies traditional word embeddings by ensuring that all target words in a phrase have exactly the same context. We demonstrate that including this information within a context window produces superior embeddings for both intrinsic evaluation tasks and downstream extrinsic tasks

Lack of NF1 expression in a sporadic schwannoma from a patient without neurofibromatosis

The neurofibromatosis type 1 (NF1) gene encodes a tumor suppressor protein, neurofibromin, which is expressed at high levels in Schwann cells and other adult tissues. Loss of NF1 expression has been reported in Schwann cell tumors (neurofibrosarcomas) from patients with NF1 and its loss is associated with increased proliferation of these cells. In this report, we describe downregulation of NF1 expression in a single spinal schwannoma from an individual without clinical features of neurofibromatosis type 1 or 2. Barely detectable expression of NF1 RNA was found in this tumor by in situ hybridization using an NF1 -specific riboprobe as well as by Northern blot and reverse-transcribed (RT)-PCR analysis. In Schwann cells cultured from this schwannoma, abundant expression of NF1 RNA could be detected by Northern blot and RT-PCR analysis. These results suggest that, in some tumors, expression of NF1 may be downregulated by factors produced within the tumor and may represent a novel mechanism for inactivating these growth suppressing genes and allowing for increased cell proliferation in tumors.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45383/1/11060_2005_Article_BF01057754.pd

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

Deep generative models provide powerful tools for distributions over complicated manifolds, such as those of natural images. But many of these methods, including generative adversarial networks (GANs), can be difficult to train, in part because they are prone to mode collapse, which means that they characterize only a few modes of the true distribution. To address this, we introduce VEEGAN, which features a reconstructor network, reversing the action of the generator by mapping from data to noise. Our training objective retains the original asymptotic consistency guarantee of GANs, and can be interpreted as a novel autoencoder loss over the noise. In sharp contrast to a traditional autoencoder over data points, VEEGAN does not require specifying a loss function over the data, but rather only over the representations, which are standard normal by assumption. On an extensive set of synthetic and real world image datasets, VEEGAN indeed resists mode collapsing to a far greater extent than other recent GAN variants, and produces more realistic samples

In Silico Design in Homogeneous Catalysis Using Descriptor Modelling

This review summarises the state-of-the-art methodologies used for designinghomogeneous catalysts and optimising reaction conditions (e.g. choosing the right solvent).We focus on computational techniques that can complement the current advances in high-throughput experimentation, covering the literature in the period 1996-2006. The reviewassesses the use of molecular modelling tools, from descriptor models based onsemiempirical and molecular mechanics calculations, to 2D topological descriptors andgraph theory methods. Different techniques are compared based on their computational andtime cost, output level, problem relevance and viability. We also review the application ofvarious data mining tools, including artificial neural networks, linear regression, andclassification trees. The future of homogeneous catalysis discovery and optimisation isdiscussed in the light of these developments