Similarity Learning for High-Dimensional Sparse Data

A good measure of similarity between data points is crucial to many tasks in machine learning. Similarity and metric learning methods learn such measures automatically from data, but they do not scale well respect to the dimensionality of the data. In this paper, we propose a method that can learn efficiently similarity measure from high-dimensional sparse data. The core idea is to parameterize the similarity measure as a convex combination of rank-one matrices with specific sparsity structures. The parameters are then optimized with an approximate Frank-Wolfe procedure to maximally satisfy relative similarity constraints on the training data. Our algorithm greedily incorporates one pair of features at a time into the similarity measure, providing an efficient way to control the number of active features and thus reduce overfitting. It enjoys very appealing convergence guarantees and its time and memory complexity depends on the sparsity of the data instead of the dimension of the feature space. Our experiments on real-world high-dimensional datasets demonstrate its potential for classification, dimensionality reduction and data exploration.Comment: 14 pages. Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS 2015). Matlab code: https://github.com/bellet/HDS

Triggering waves in nonlinear lattices: Quest for anharmonic phonons and corresponding mean free paths

Guided by a stylized experiment we develop a self-consistent anharmonic phonon concept for nonlinear lattices which allows for explicit "visualization." The idea uses a small external driving force which excites the front particles in a nonlinear lattice slab and subsequently one monitors the excited wave evolution using molecular dynamics simulations. This allows for a simultaneous, direct determination of the existence of the phonon mean free path with its corresponding anharmonic phonon wavenumber as a function of temperature. The concept for the mean free path is very distinct from known prior approaches: the latter evaluate the mean free path only indirectly, via using both, a scale for the phonon relaxation time and yet another one for the phonon velocity. Notably, the concept here is neither limited to small lattice nonlinearities nor to small frequencies. The scheme is tested for three strongly nonlinear lattices of timely current interest which either exhibit normal or anomalous heat transport

Contact killing of bacterial pathogens on metallic copper : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Microbiology at Massey University, Auckland, New Zealand

Hospital-acquired infections (HAIs) are a serious health concern worldwide. Currently in New Zealand, about one in ten patients admitted to hospitals will acquire an infection while receiving treatments for other medical or surgical conditions. An emerging strategy for HAIs prevention is to use self-sanitising copper surfaces on items commonly touched in hospitals, which can provide sustained protection against microbial contamination. This is due to the fact that a wide range of microorganisms can be rapidly killed on copper in a process termed “contact killing”. However, the mechanisms of copper-mediated contact killing are not fully understood; and moreover, the potential of bacterial pathogens to develop resistance to metallic copper has so far not been examined. Here we hypothesize that bacteria are predominantly killed by a burst release of toxic copper ions resulted from chemical reactions between surface components of bacterial cell and metallic copper. To test this copper ion burst release hypothesis, we isolated and phenotypically characterized small colony variants (SCVs) derived from the two most common nosocomial pathogens, Staphylococcus aureus and Pseudomonas aeruginosa. Consistent to our expectation, SCV mutants overproducing exopolysaccharides (EPS) are more rapidly killed than wild type on the surfaces of pure copper (99.9% Cu) and brass (63.5% Cu). Similar results were obtained with a panel of mutants with altered production of cell surface components (EPS, lipopolysaccharides, capsules, flagella and pili) in a non-pathogenic model organism of Pseudomonas fluorescens SBW25. Next, a unique approach of experimental evolution was used to assess the potential emergence of bacterial resistance to metallic copper. Specifically, P. fluorescens SBW25 was subjected to daily passage of sub-lethal conditions on the surfaces of brass. After 100 daily transfers, the evolved strains had a slight increase of survival rate on brass; but importantly, ~97% of cells can still be killed on brass within one hour. Taken together, our results clearly indicate that the rate of bacterial killing on copper is largely determined by surface components of a bacterial cell, providing support for the copper ion burst release hypothesis. Our primary data of experimental evolution showed that bacteria have limited ability to evolve resistance to metallic copper

Attention Correctness in Neural Image Captioning

Attention mechanisms have recently been introduced in deep learning for various tasks in natural language processing and computer vision. But despite their popularity, the "correctness" of the implicitly-learned attention maps has only been assessed qualitatively by visualization of several examples. In this paper we focus on evaluating and improving the correctness of attention in neural image captioning models. Specifically, we propose a quantitative evaluation metric for the consistency between the generated attention maps and human annotations, using recently released datasets with alignment between regions in images and entities in captions. We then propose novel models with different levels of explicit supervision for learning attention maps during training. The supervision can be strong when alignment between regions and caption entities are available, or weak when only object segments and categories are provided. We show on the popular Flickr30k and COCO datasets that introducing supervision of attention maps during training solidly improves both attention correctness and caption quality, showing the promise of making machine perception more human-like.Comment: To appear in AAAI-17. See http://www.cs.jhu.edu/~cxliu/ for supplementary materia

Table-to-text Generation by Structure-aware Seq2seq Learning

Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the \texttt{WIKIBIO} dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on https://github.com/tyliupku/wiki2bio.Comment: Accepted by AAAI201