Character-Aware Neural Language Models

We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long short-term memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-of-the-art despite having 60% fewer parameters. On languages with rich morphology (Arabic, Czech, French, German, Spanish, Russian), the model outperforms word-level/morpheme-level LSTM baselines, again with fewer parameters. The results suggest that on many languages, character inputs are sufficient for language modeling. Analysis of word representations obtained from the character composition part of the model reveals that the model is able to encode, from characters only, both semantic and orthographic information.Comment: AAAI 201

Adapting Sequence Models for Sentence Correction

In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches. Our strongest sequence-to-sequence model improves over our strongest phrase-based statistical machine translation model, with access to the same data, by 6 M2 (0.5 GLEU) points. Additionally, in the data environment of the standard CoNLL-2014 setup, we demonstrate that modeling (and tuning against) diffs yields similar or better M2 scores with simpler models and/or significantly less data than previous sequence-to-sequence approaches.Comment: EMNLP 201

The structures and dynamics of atomic and molecular adsorbates on metal surfaces by scanning tunneling microscopy and low energy electron diffraction

Studies of surface structure and dynamics of atoms and molecules on metal surfaces are presented. My research has focused on understanding the nature of adsorbate-adsorbate and adsorbate-substrate interactions through surface studies of coverage dependency and coadsorption using both scanning tunneling microscopy (STM) and low energy electron diffraction (LEED). The effect of adsorbate coverage on the surface structures of sulfur on Pt(111) and Rh(111) was examined. On Pt(111), sulfur forms p(2x2) at 0.25 ML of sulfur, which transforms into a more compressed ({radical}3x{radical}3)R30{degrees} at 0.33 ML. On both structures, it was found that sulfur adsorbs only in fcc sites. When the coverage of sulfur exceeds 0.33 ML, it formed more complex c({radical}3x7)rect structure with 3 sulfur atoms per unit cell. In this structure, two different adsorption sites for sulfur atoms were observed - two on fcc sites and one on hcp site within the unit cell

Characterization of Phagocytic Pattern Recognition Receptors in Drosophila melanogaster

Drosophila melanogaster has emerged as a powerful model system to study innate immunity. Insects employ multilayered innate immune defenses including antimicrobial peptide responses and phagocytosis. In Drosophila, phagocytosis is carried out by plasmatocytes, a blood cell type similar to mammalian macrophages and neutrophils. The scavenger receptor Eater is expressed by larval and adult plasmatocytes and mediates recognition of a broad range of bacterial pathogens. Eater is required for fly survival after infection with Gram-positive and Gram-negative bacteria. However, the bacterial ligands of Eater, and the mechanisms by which this receptor recognizes these different types of bacteria, remain poorly understood. To address this problem, I generated a soluble, Fc-tagged receptor variant of Eater comprising the N-terminal 199 amino acids (including four N-terminal EGF-like repeats) and raised antibodies against Eater. Using these tools, I established (i) that Eater is expressed on the surface of macrophage-like Drosophila S2 cells, (ii) that it interacts with broad, yet distinct classes of heat- and ethanol-inactivated microbes and (iii) that it binds peptidoglycan from Gram-negative Proteobacteria (E. coli) and Gram-positive Firmicutes (E. faecalis and S. aureus), but not Gram-positive Actinobacteria (M. luteus). In order to identify genes involved in the phagocytosis of M. luteus, I screened 39 candidate genes by RNA interference-mediated knock down in S2 cells. A longstanding question was whether Eater recognizes live, naïve bacteria. I found that Eater-Fc bound equally well to naïve or heat-inactivated S. aureus or E. faecalis, suggesting that in vivo Eater directly targets live Gram-positive bacteria, enabling their phagocytic clearance and destruction. By contrast, Eater-Fc was unable to interact with live Gram-negative bacteria (E. coli, S. marcescens and P. aeruginosa). Eater binding required prior membrane-disrupting treatments. Cecropin A, a prototypic cationic, membrane-disrupting antimicrobial peptide could promote Eater-Fc binding to live E. coli, even at sublethal concentrations. These results suggest a previously unrecognized mechanism by which antimicrobial peptides cooperate with phagocytic receptors