We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a nontrivial task. Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventional method for generating negative facts, but the majority of the generated negative facts can be easily discriminated from positive facts, and will contribute little towards the training. Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator in GANs. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks. In experiments, we adversarially train two translation-based models, TRANSE and TRANSD, each with assistance from one of the two probability-based models, DISTMULT and COMPLEX. We evaluate the performances of KBGAN on the link prediction task, using three knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental results show that adversarial training substantially improves the performances of target embedding models under various settings.

Tumor cells rely on aerobic glycolysis as their main energy resource (Warburg effect). Recent research has highlighted the importance of lipid metabolism in tumor progression, and certain cancers even turn to fatty acids as the main fuel. Related studies have identified alterations of fatty acid metabolism in human bladder cancer (BCa). Our microarray analysis showed that fatty acid metabolism was activated in BCa compared with normal bladder. The free fatty acid (FFA) level was also increased in BCa compared with paracancerous tissues. Inhibition of fatty acid oxidation (FAO) with etomoxir caused lipid accumulation, decreased adenosine triphosphate (ATP) and nicotinamide adenine dinucleotide phosphate (NADPH) levels, suppressed BCa cell growth in vitro and in vivo, and reduced motility of BCa cells via affecting epithelial-mesenchymal transition (EMT)-related proteins. Furthermore, etomoxir induced BCa cell cycle arrest at G 0 /G 1 phase through peroxisome proliferator-activated receptor (PPAR) γ-mediated pathway with alterations in fatty acid metabolism associated gene expression. The cell cycle arrest could be reversed by PPARγ antagonist GW9662. Taken together, our results suggest that inhibition of FAO with etomoxir may provide a novel avenue to investigate new therapeutic approaches to human BCa.

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We propose the task of unsupervised morphological paradigm completion. Given only raw text and a lemma list, the task consists of generating the morphological paradigms, i.e., all inflected forms, of the lemmas. From a natural language processing (NLP) perspective, this is a challenging unsupervised task, and high-performing systems have the potential to improve tools for low-resource languages or to assist linguistic annotators. From a cognitive science perspective, this can shed light on how children acquire morphological knowledge. We further introduce a system for the task, which generates morphological paradigms via the following steps: (i) EDIT TREE retrieval, (ii) additional lemma retrieval, (iii) paradigm size discovery, and (iv) inflection generation. We perform an evaluation on 14 typologically diverse languages. Our system outperforms trivial baselines with ease and, for some languages, even obtains a higher accuracy than minimally supervised systems. 1

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