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We consider the automated recognition of human actions in surveillance videos. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. Convolutional neural networks (CNNs) are a type of deep model that can act directly on the raw inputs. However, such models are currently limited to handling 2D inputs. In(More)
In this paper, we propose a novel document clustering method based on the non-negative factorization of the term-document matrix of the given document corpus. In the latent semantic space derived by the non-negative matrix factorization (NMF), each axis captures the base topic of a particular document cluster, and each document is represented as an additive(More)
In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). Our work is the first to apply a bidirectional LSTM CRF (denoted(More)
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated according to this distribution. The model consists of two sub-networks: a deep recurrent neural network for(More)
IDH1 and IDH2 mutations occur frequently in gliomas and acute myeloid leukemia, leading to simultaneous loss and gain of activities in the production of α-ketoglutarate (α-KG) and 2-hydroxyglutarate (2-HG), respectively. Here we demonstrate that 2-HG is a competitive inhibitor of multiple α-KG-dependent dioxygenases, including histone demethylases and the(More)
Protein lysine acetylation has emerged as a key posttranslational modification in cellular regulation, in particular through the modification of histones and nuclear transcription regulators. We show that lysine acetylation is a prevalent modification in enzymes that catalyze intermediate metabolism. Virtually every enzyme in glycolysis, gluconeogenesis,(More)
Heterozygous mutations in the gene encoding isocitrate dehydrogenase-1 (IDH1) occur in certain human brain tumors, but their mechanistic role in tumor development is unknown. We have shown that tumor-derived IDH1 mutations impair the enzyme's affinity for its substrate and dominantly inhibit wild-type IDH1 activity through the formation of catalytically(More)
The identification of genetic factors underlying the complex responses of plants to drought stress provides a solid basis for improving drought resistance. The stay-green character in sorghum (Sorghum bicolor L. Moench) is a post-flowering drought resistance trait, which makes plants resistant to premature senescence under drought stress during the(More)
In this paper, we present the mQA model, which is able to answer questions about the content of an image. The answer can be a sentence, a phrase or a single word. Our model contains four components: a Long Short-Term Memory (LSTM) to extract the question representation, a Convolutional Neural Network (CNN) to extract the visual representation, an LSTM for(More)
We present an approach that exploits hierarchical Recurrent Neural Networks (RNNs) to tackle the video captioning problem, i.e., generating one or multiple sentences to describe a realistic video. Our hierarchical framework contains a sentence generator and a paragraph generator. The sentence generator produces one simple short sentence that describes a(More)