Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition

End-to-end training of deep learning-based models allows for implicit learning of intermediate representations based on the final task loss. However, the end-to-end approach ignores the useful domain knowledge encoded in explicit intermediate-level supervision. We hypothesize that using intermediate representations as auxiliary supervision at lower levels of deep networks may be a good way of combining the advantages of end-to-end training and more traditional pipeline approaches. We present experiments on conversational speech recognition where we use lower-level tasks, such as phoneme recognition, in a multitask training approach with an encoder-decoder model for direct character transcription. We compare multiple types of lower-level tasks and analyze the effects of the auxiliary tasks. Our results on the Switchboard corpus show that this approach improves recognition accuracy over a standard encoder-decoder model on the Eval2000 test set

A Study of All-Convolutional Encoders for Connectionist Temporal Classification

Connectionist temporal classification (CTC) is a popular sequence prediction approach for automatic speech recognition that is typically used with models based on recurrent neural networks (RNNs). We explore whether deep convolutional neural networks (CNNs) can be used effectively instead of RNNs as the "encoder" in CTC. CNNs lack an explicit representation of the entire sequence, but have the advantage that they are much faster to train. We present an exploration of CNNs as encoders for CTC models, in the context of character-based (lexicon-free) automatic speech recognition. In particular, we explore a range of one-dimensional convolutional layers, which are particularly efficient. We compare the performance of our CNN-based models against typical RNNbased models in terms of training time, decoding time, model size and word error rate (WER) on the Switchboard Eval2000 corpus. We find that our CNN-based models are close in performance to LSTMs, while not matching them, and are much faster to train and decode.Comment: Accepted to ICASSP-201

GEOSIM: A numerical model for geophysical fluid flow simulation

A numerical model which simulates geophysical fluid flow in a wide range of problems is described in detail, and comparisons of some of the model's results are made with previous experimental and numerical studies. The model is based upon the Boussinesq Navier-Stokes equations in spherical coordinates, which can be reduced to a cylindrical system when latitudinal walls are used near the pole and the ratio of latitudinal length to the radius of the sphere is small. The equations are approximated by finite differences in the meridional plane and spectral decomposition in the azimuthal direction. The user can specify a variety of boundary and initial conditions, and there are five different spectral truncation options. The results of five validation cases are presented: (1) the transition between axisymmetric flow and baroclinic wave flow in the side heated annulus; (2) the steady baroclinic wave of the side heated annulus; (3) the wave amplitude vacillation of the side heated annulus; (4) transition to baroclinic wave flow in a bottom heated annulus; and (5) the Spacelab Geophysical Fluid Flow Cell (spherical) experiment

Karen H. Lu, MD

https://openworks.mdanderson.org/legendsandlegacieschapters/1014/thumbnail.jp

Probiotics and Crohn’s Disease

Crohn’s disease (CD) is a chronic inflammatory condition that can affect any part of the gastrointestinal tract. The human gut microbiome is altered in patients with Crohn’s disease. This knowledge has led to research directed at altering the microbiome for therapeutic potential. Probiotics are an attractive therapy, both from a researcher’s perspective and also from the patients’ perspective. In this chapter, we will review the current clinical evidence for the use of probiotics in the treatment of Crohn’s disease. These studies are divided into three categories: induction of remission, maintenance of medically induced remission, and maintenance of surgically induced remission. Unfortunately, there is insufficient evidence to support the use of probiotics in the management of Crohn’s disease at this time

Two-Dimensional Controlled Syntheses of Polypeptide Molecular Brushes via N-Carboxyanhydride Ring-Opening Polymerization and Ring-Opening Metathesis Polymerization.

Well-defined molecular brushes bearing polypeptides as side chains were prepared by a "grafting through" synthetic strategy with two-dimensional control over the brush molecular architectures. By integrating N-carboxyanhydride ring-opening polymerizations (NCA ROPs) and ring-opening metathesis polymerizations (ROMPs), desirable segment lengths of polypeptide side chains and polynorbornene brush backbones were independently constructed in controlled manners. The N2 flow accelerated NCA ROP was utilized to prepare polypeptide macromonomers with different lengths initiated from a norbornene-based primary amine, and those macromonomers were then polymerized via ROMP. It was found that a mixture of dichloromethane and an ionic liquid were required as the solvent system to allow for construction of molecular brush polymers having densely-grafted peptide chains emanating from a polynorbornene backbone, poly(norbornene-graft-poly(β-benzyl-l-aspartate)) (P(NB-g-PBLA)). Highly efficient postpolymerization modification was achieved by aminolysis of PBLA side chains for facile installment of functional moieties onto the molecular brushes