Non-Autoregressive Machine Translation with Auxiliary Regularization

As a new neural machine translation approach, Non-Autoregressive machine Translation (NAT) has attracted attention recently due to its high efficiency in inference. However, the high efficiency has come at the cost of not capturing the sequential dependency on the target side of translation, which causes NAT to suffer from two kinds of translation errors: 1) repeated translations (due to indistinguishable adjacent decoder hidden states), and 2) incomplete translations (due to incomplete transfer of source side information via the decoder hidden states). In this paper, we propose to address these two problems by improving the quality of decoder hidden representations via two auxiliary regularization terms in the training process of an NAT model. First, to make the hidden states more distinguishable, we regularize the similarity between consecutive hidden states based on the corresponding target tokens. Second, to force the hidden states to contain all the information in the source sentence, we leverage the dual nature of translation tasks (e.g., English to German and German to English) and minimize a backward reconstruction error to ensure that the hidden states of the NAT decoder are able to recover the source side sentence. Extensive experiments conducted on several benchmark datasets show that both regularization strategies are effective and can alleviate the issues of repeated translations and incomplete translations in NAT models. The accuracy of NAT models is therefore improved significantly over the state-of-the-art NAT models with even better efficiency for inference.Comment: AAAI 201

The 2nd Place Solution for 2023 Waymo Open Sim Agents Challenge

In this technical report, we present the 2nd place solution of 2023 Waymo Open Sim Agents Challenge (WOSAC)[4]. We propose a simple yet effective autoregressive method for simulating multi-agent behaviors, which is built upon a well-known multimodal motion forecasting framework called Motion Transformer (MTR)[5] with postprocessing algorithms applied. Our submission named MTR+++ achieves 0.4697 on the Realism Meta metric in 2023 WOSAC. Besides, a modified model based on MTR named MTR_E is proposed after the challenge, which has a better score 0.4911 and is ranked the 3rd on the leaderboard of WOSAC as of June 25, 2023

Realized Volatility and Stylized Facts of Chinese Treasury Bond Market

Based on high frequency data, this paper studies the volatility stylized facts of Chinese Treasury bond market (CTBM) in detail, including the best sampling frequency selected to compute the realized volatility, the conditional and unconditional distribution of the returns, the long memory property, the intraday, inter-day pattern of the returns and volatility, the asymmetry of volatility, and so on. The main conclusions about CTBM volatility are provided. 15 minute is best sampling frequency. The RV-based conditional distribution of return is nearly normal. Both return and volatility have significant inter-day but insignificant intraday periodicity. Moreover, the volatility asymmetry existing widely in stock or exchange market is not significant in Chinese Treasury bond market. Key words: Realized volatility, Chinese Treasury bond market, High frequency data Résumé: Basé sur des données de haute fréquence, le présent article étudie en détail la volatilité des faits stylisés du Marché de bon du Trésor chinois (MBTC), comprenant la meilleure fréquence de prélèvement sélectionnée pour calculer la volatilité réalisée, la distribution conditionnelle et inconditionnelle des retours, la propriété de longue mémoire, le modèle intrajour et interjour des retours et la volatilité, l’asymétrie de volatilité, etc. Les conclusions principales sur la volatilité du MBTC sont les suivantes : 15 minutes est la meilleure fréquence de prélèvement, la distribution conditionnelle RV-basé du retour est presque normale. Le retour et l’asymétrie de volatilité ont tous les deux une périodicité inter-jour signifiante, mais une périodicité intrajour insignifiante. D’ailleurs, l’asymétrie de volatilité existant amplement dans la bourse et le marché des changes n’est pas importante sur le Marché de bon du Trésor chinois. Mots-Clés: volatilité réalisée, Marché de bon du Trésor chinois, données de haute fréquenc

Care Network Coordination for Chemotherapy at Home: A Case Study

International audienceThis paper deals with a system of chemotherapy at home which is managed by a Home Care Services (HCS) structure. The main role of this HCS structure is to coordinate care actors for a smooth organization of chemotherapy at home. In this work, we model a real system of chemotherapy at home managed by a HCS structure, and simulate its behaviour. The objective is to evaluate the relevance of such a system for current activities of the HCS structure, and to propose solutions for improving the optimal coordination of the care network for chemotherapy at home

Simulations reveal the role of composition into the atomic-level flexibility of bioactive glass cements

K. V. T. thanks ETT-489/2009 and TAMOP-4.2.1.B, Hungary. D. D. T. thanks the UK's Royal Society for the award of a Royal Society Industry Fellowship. This research utilised Queen Mary's MidPlus computational facilities, supported by QMUL Research-IT and funded by EPSRC grant EP/K000128/1. Via our membership of the UK's HEC Materials Chemistry Consortium, which is funded by EPSRC (EP/L000202), this work used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk)