We study reconfiguration problems for cliques in a graph, which determine whether there exists a sequence of cliques that transforms a given clique i…

Suppose that we are given two independent sets $I_0$ and $I_r$ of a graph such that $|I_0| = |I_r|$, and imagine that a token is placed on each verte…

Consider an undirected graph modeling a social network, where the vertices represent users, and the edges do connections among them. In the competiti…

Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ab…

We have made a relatively simple modification to the native-code OCaml compiler to specialize generic array accesses after inlining, and observed modest or significant speed-ups for numerical programs.

We have implemented an optimization that specializes type-generic array accesses after inlining of polymorphic functions in the native-code OCaml com…

The research trend in Japanese predicate-argument structure (PAS) analysis is shifting from pointwise prediction models with local features to global…

We have investigated the hardness and possibilities of precisely checking and inferring quantitative information flow according to the various definitions proposed in literature. Specifically, we have considered the definitions based on the Shannon …

Researchers have proposed formal definitions of quantitative information flow based on information theoretic notions such as the Shannon entropy, the…

This paper proposes a state-of-the-art recurrent neural network (RNN) language model that combines probability distributions computed not only from a…

The electric manipulation of antiferromagnets has become an area of great interest recently for zero-stray-field spintronic devices, and for their ri…

Embedding models for entities and relations are extremely useful for recovering missing facts in a knowledge base. Intuitively, a relation can be mod…

Recent studies revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets. This allo…

Recent machine translation algorithms mainly rely on parallel corpora. However, since the availability of parallel corpora remains limited, only some…

This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner. We propose extending the continu…

In this work, we investigate several neural network architectures for fine-grained entity type classification. Particularly, we consider extensions t…

Suppose that we are given two dominating sets $D_s$ and $D_t$ of a graph $G$ whose cardinalities are at most a given threshold $k$. Then, we are aske…

Learning distributed representations for relation instances is a central technique in downstream NLP applications. In order to address semantic model…

We introduced a monotonicity-driven NLI data augmentation method. The experiments showed that neural models trained on HELP obtained the higher overall accuracy. However, the improvement tended to be small on downward monotone inferences with disju…

Large crowdsourced datasets are widely used for training and evaluating neural models on natural language inference (NLI). Despite these efforts, neu…

Given a dominating set, how much smaller a dominating set can we find through elementary operations? Here, we proceed by iterative vertex addition an…

Browsing news articles on multiple devices is now possible. The lengths of news article headlines have precise upper bounds, dictated by the size of …

We present in this paper our approach for modeling inter-topic preferences of Twitter users: for example, those who agree with the Trans-Pacific Part…

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