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A common approach to sentiment classification is to identify a set of sentiment-carrying words and then to use machine learning to build a classifier that can classify sentiment based on the presence/absence of those words. In this paper, we propose a Fourier-based extension of this approach. Specifically, we introduce a spectral learning algorithm that implicitly identifies sentiment-carrying words...
Spectral learning algorithms learn an unknown function by learning a spectral (e.g., Fourier) representation of the function. However, there are many possible spectral representations, none of which will be best in all situations. Consequently, it seems natural to consider how a spectral learner could make use of multiple representations when learning. This paper proposes and compares three approaches...
Therapy-resistant microenvironments represent a major barrier toward effective elimination of disseminated malignancies. Here, we show that select microenvironments can underlie resistance to antibody-based therapy. Using a humanized model of treatment refractory B cell leukemia, we find that infiltration of leukemia cells into the bone marrow rewires the tumor microenvironment to inhibit engulfment...
In this paper, we explore the problem of how to learn spectral (e.g., Fourier) models for classification problems. Specifically, we consider two sub-problems of spectral learning: (1) how to select the basis functions that will be included in the model and (2) how to assign coefficients to the selected basis functions. Interestingly, empirical results suggest that the most commonly used approach does...
The discovery of host-microRNA (miRNA) targets in the genomes of many vertebrate viruses indicates that the corresponding miRNAs are a part of the host's innate antiviral defense. However, given that viruses evolve much faster than host miRNAs, it is surprising that viral variants lacking these ‘antiviral’ miRNA target sequences have not become established. We present an alternate view that miRNAs...
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