Bidirectional Decoding for Statistical Machine Translation

This paper describes the right-to-left decoding method, which translates an input string by generating in right-to-left direction. In addition, presented is the bidirectional decoding method, that can take both of the advantages of left-to-right and right-to-left decoding method by generating output in both ways and by merging hypothesized partial outputs of two directions. The experimental results on Japanese and English translation showed that the right-to-left was better for Englith-to-Japanese translation, while the left-to-right was suitable for Japanese-to-English translation. It was also observed that the bidirectional method was better for English-to-Japanese translation

Table and Image Generation for Investigating Knowledge of Entities in Pre-trained Vision and Language Models

In this paper, we propose a table and image generation task to verify how the knowledge about entities acquired from natural language is retained in Vision & Language (V & L) models. This task consists of two parts: the first is to generate a table containing knowledge about an entity and its related image, and the second is to generate an image from an entity with a caption and a table containing related knowledge of the entity. In both tasks, the model must know the entities used to perform the generation properly. We created the Wikipedia Table and Image Generation (WikiTIG) dataset from about 200,000 infoboxes in English Wikipedia articles to perform the proposed tasks. We evaluated the performance on the tasks with respect to the above research question using the V & L model OFA, which has achieved state-of-the-art results in multiple tasks. Experimental results show that OFA forgets part of its entity knowledge by pre-training as a complement to improve the performance of image related tasks.Comment: Accepted at ACL 202

Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?

Knowledge graphs (KGs) consist of links that describe relationships between entities. Due to the difficulty of manually enumerating all relationships between entities, automatically completing them is essential for KGs. Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities in a KG. Traditional embedding-based KGC methods, such as RESCAL, TransE, DistMult, ComplEx, RotatE, HAKE, HousE, etc., infer missing links using only the knowledge from training data. In contrast, the recent Pre-trained Language Model (PLM)-based KGC utilizes knowledge obtained during pre-training. Therefore, PLM-based KGC can estimate missing links between entities by reusing memorized knowledge from pre-training without inference. This approach is problematic because building KGC models aims to infer unseen links between entities. However, conventional evaluations in KGC do not consider inference and memorization abilities separately. Thus, a PLM-based KGC method, which achieves high performance in current KGC evaluations, may be ineffective in practical applications. To address this issue, we analyze whether PLM-based KGC methods make inferences or merely access memorized knowledge. For this purpose, we propose a method for constructing synthetic datasets specified in this analysis and conclude that PLMs acquire the inference abilities required for KGC through pre-training, even though the performance improvements mostly come from textual information of entities and relations.Comment: 15 pages, 10 figure

Model-based Subsampling for Knowledge Graph Completion

Subsampling is effective in Knowledge Graph Embedding (KGE) for reducing overfitting caused by the sparsity in Knowledge Graph (KG) datasets. However, current subsampling approaches consider only frequencies of queries that consist of entities and their relations. Thus, the existing subsampling potentially underestimates the appearance probabilities of infrequent queries even if the frequencies of their entities or relations are high. To address this problem, we propose Model-based Subsampling (MBS) and Mixed Subsampling (MIX) to estimate their appearance probabilities through predictions of KGE models. Evaluation results on datasets FB15k-237, WN18RR, and YAGO3-10 showed that our proposed subsampling methods actually improved the KG completion performances for popular KGE models, RotatE, TransE, HAKE, ComplEx, and DistMult.Comment: Accepted by AACL 2023; 9 pages, 3 figures, 5 table

Magnetic properties of epitaxial Fe3_3O4_4 films with various crystal orientations and TMR effect in room temperature

Fe$_3$O$_4$ is a ferrimagnetic spinel ferrite that exhibits electric conductivity at room temperature (RT). Although the material has been predicted to be a half metal according to ab-initio calculations, magnetic tunnel junctions (MTJs) with Fe$_3$O$_4$ electrodes have demonstrated a small tunnel magnetoresistance effect. Not even the sign of the TMR ratio has been experimentally established. Here, we report on the magnetic properties of epitaxial Fe$_3$O$_4$ films with various crystal orientations. The films exhibited apparent crystal orientation dependence on hysteresis curves. In particular, Fe$_3$O$_4$(110) films exhibited in-plane uniaxial magnetic anisotropy. With respect to the squareness of hysteresis, Fe$_3$O$_4$ (111) demonstrated the largest squareness. Furthermore, we fabricated MTJs with Fe$_3$O$_4$(110) electrodes, and obtained an TMR effect of -12\% at RT. The negative TMR ratio corresponded to the negative spin polarization of Fe$_3$O$_4$ predicted from band calculations

Respective roles of Kr-h1, Br and E93 in hemimetabolous metamorphosis

Juvenile hormones (JHs) and the genetic interaction between the transcription factors Krüppel homolog 1 (Kr-h1) and Broad (Br) regulate the transformation of insects from immature to adult forms in both types of metamorphosis (holometaboly with a pupal stage versus hemimetaboly with no pupal stage); however, knowledge about the exact instar in which this occurs is limited. Using the hemimetabolous cricket Gryllus bimaculatus (Gb), we demonstrate that a genetic interaction occurs among Gb’Kr-h1, Gb’Br and the adult-specifier transcription factor Gb’E93 from the sixth to final (eighth) nymphal instar. Gb’Kr-h1 and Gb’Br mRNAs were strongly expressed in the abdominal tissues of sixth instar nymphs, with precocious adult moults being induced by Gb’Kr-h1 or Gb’Br knockdown in the sixth instar. Depletion of Gb’Kr-h1 or Gb’Br up-regulates Gb’E93 in the sixth instar. In contrast, Gb’E93 knockdown at the sixth instar prevents nymphs transitioning to adults, instead producing supernumerary nymphs. Gb’E93 also represses Gb’Kr-h1 and Gb’Br expression in the penultimate nymphal instar, demonstrating its important role in adult differentiation. Our results suggest that the regulatory mechanisms underlying the pupal transition in holometabolous insects are evolutionarily conserved in hemimetabolous G. bimaculatus, with the penultimate and final nymphal periods being equivalent to the pupal stage

A Training Method for the Speech Controlled Environmental Control System Based on Candidate Word Discriminations

This paper proposes a concept of a training system for the speech controlled environmental control system: Bio-Remote based on candidate word discriminations. The proposed system can provide three-types of voice signal training: (1) volume, (2) tempo/timing and (3) candidate word which are important for accurate speech recognition based on false recognition results. During the training, such three kinds of features are extracted from measured voice signals and visually and auditory fed back to the user in real time. This allows the user to train speech abilities even if false recognition results are extracted because of slurred speech. The efficacy of the proposed system was demonstrated through training experiments for slurred speech conducted with healthy participants. The results showed that the proposed system was capable for the training of speech abilities.This work was partially supported by JSPS/MEXT KAKENHI Grant Numbers 17K12723 and 26330226

Cyclooxygenase 2 Modulates Killing of Cytotoxic T Lymphocytes by Colon Cancer Cells

Although anti-cancer effects of cyclooxygenase 2 (COX2) inhibitors have been reported, most studies focused on the direct effects of COX2 inhibiters on colon cancer cells. On the other hand, several types of cancers express Fas ligand (FasL) and/or TRAIL and mediate apoptosis of T cells in vitro. The “counter-attack” machinery may account for the mechanisms by which tumors evade host immune surveillance. In this study we determined if COX2 inhibitor could modulate effector molecules of cell death on colon cancer cells changing their effects on cytotoxic T lymphocytes. Colon adenocarcinoma cells, HCA7 and HCT116, the former COX2-positive and the latter COX2-negative, were pre-incubated with/without a COX2 inhibitor, NS398. Subsequently, the cells were co-cultured with Jurkat T cell leukemia cells and damage to Jurkat cells was determined. Treatment with NS398 resulted in reduction of expression of FasL and TRAIL in HCA7 cells, whereas NS398 did not affect the expression of FasL and TRAIL in HCT116 cells. The number of viable Jurkat cells was diminished when cells were co-cultured with naive, non-pretreated HCA7 or HCA116 cells. Preincubation of HCA7 cells with NS398 before co-culture blunted the HCA7 cell-induced cell toxicity on Jurkat cells. In contrast, pretreatment with NS398 failed to inhibit the HCT116-induced Jurkat cell killing. Our results suggest that COX2 regulates the expression of FasL and TRAIL on COX2-positive colon cancer cells thereby evoking a counter-attack against cytotoxic T cells, which may lead to compromised host immune responses