Orthographic and Phonological Effects in the Picture–word Interference Paradigm: Evidence From a Logographic Language

One important finding with the picture–word interference paradigm is that picture-naming performance is facilitated by the presentation of a distractor (e.g., CAP) formally related to the picture name (e.g., “cat”). In two picture-naming experiments we investigated the nature of such form facilitation effect with Mandarin Chinese, separating the effects of phonology and orthography. Significant facilitation effects were observed both when distractors were only orthographically or only phonologically related to the targets. The orthographic effect was overall stronger than the phonological effect. These findings suggest that the classic form facilitation effect in picture–word interference is a mixed effect with multiple loci: it cannot be attributed merely to the nonlexical activation of the target phonological segments from the visual input of the distractor. It seems instead that orthographically only related distractors facilitate the lexical selection process of picture naming, and phonologically only related distractors facilitate the retrieval of target phonological segments.Psycholog

An Ensemble Model with Ranking for Social Dialogue

Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real customers get to rate systems developed by leading universities worldwide. The aim of the challenge is to converse "coherently and engagingly with humans on popular topics for 20 minutes". We describe our Alexa Prize system (called 'Alana') consisting of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose a system response. The ranker was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition.Comment: NIPS 2017 Workshop on Conversational A

A Tri-network Model of Human Semantic Processing

Humans process the meaning of the world via both verbal and nonverbal modalities. It has been established that widely distributed cortical regions are involved in semantic processing, yet the global wiring pattern of this brain system has not been considered in the current neurocognitive semantic models. We review evidence from the brain-network perspective, which shows that the semantic system is topologically segregated into three brain modules. Revisiting previous region-based evidence in light of these new network findings, we postulate that these three modules support multimodal experiential representation, language-supported representation, and semantic control. A tri-network neurocognitive model of semantic processing is proposed, which generates new hypotheses regarding the network basis of different types of semantic processes

TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning

Despite recent progress in reinforcement learning (RL) from raw pixel data, sample inefficiency continues to present a substantial obstacle. Prior works have attempted to address this challenge by creating self-supervised auxiliary tasks, aiming to enrich the agent's learned representations with control-relevant information for future state prediction. However, these objectives are often insufficient to learn representations that can represent the optimal policy or value function, and they often consider tasks with small, abstract discrete action spaces and thus overlook the importance of action representation learning in continuous control. In this paper, we introduce TACO: Temporal Action-driven Contrastive Learning, a simple yet powerful temporal contrastive learning approach that facilitates the concurrent acquisition of latent state and action representations for agents. TACO simultaneously learns a state and an action representation by optimizing the mutual information between representations of current states paired with action sequences and representations of the corresponding future states. Theoretically, TACO can be shown to learn state and action representations that encompass sufficient information for control, thereby improving sample efficiency. For online RL, TACO achieves 40% performance boost after one million environment interaction steps on average across nine challenging visual continuous control tasks from Deepmind Control Suite. In addition, we show that TACO can also serve as a plug-and-play module adding to existing offline visual RL methods to establish the new state-of-the-art performance for offline visual RL across offline datasets with varying quality

The carbohydrate-insulin model does not explain the impact of varying dietary macronutrients on the body weight and adiposity of mice

Acknowledgments This study was funded by the Chinese Academy of Sciences Strategic Program (XDB13030100), the K.C. Wong Foundation, the 1000 Talents Program, and a Wolfson Merit Award to J.R.S. We thank those in the molecular energetics group in Beijing who contributed to handling the animals and the measurement of their food intake and body weight, including L. Li, B. Li, M. Li, G. Wang, X. Zhang, J. Li, C. Niu, E. Couper, A. Whittington-Davies, and M. Mazidi. Author contributions S.H. was involved in the initial experiment design, conducted experiment one, analyzed the data from experiments one and two, performed the IPA-related analysis, and co-wrote the manuscript. L.W. was involved in the sample collection for experiments one and two and conducted the RNA extractions and the RNA-seq. J.T. was involved in the sample and data collection for experiments one and two. D.Y. and Y.X. performed the initial data collection and glucose measurements for experiment two. Y.W. conducted the insulin measurements and was involved in the initial data collection for experiment two. A.D. was involved in the RNA-seq-related analysis. J.R.S. directed both projects, conceived and designed the experiments, contributed to the data analysis, and co-wrote the paper. All of the authors approved the final version prior to submission for publication.Peer reviewedPublisher PD

The mechanical properties of Polyvinyl Butyral (PVB) at high strain rates

Polyvinyl Butyral (PVB) has been largely used as an interlayer material for laminated glass to mitigate the hazard from shattered glass fragments, due to its excellent ductility and adhesive property with glass pane. With increasing threats from terrorist bombing and debris impact, the application of PVB laminated safety glass has been extended from quasi-static loading to impact and blast loading regimes, which has led to the requirement for a better understanding of PVB material properties at high strain rates. In this study, the mechanical properties of PVB are investigated experimentally over a wide range of strain rates. Firstly, quasi-static tensile tests is performed using conventional hydraulic machine at strain rates of 0.008–0.317 s−1. Then high-speed tensile test is carried out using a high-speed servo-hydraulic testing machine at strain rates from 8.7 s−1 to 1360 s−1. It is found that under quasi-static tensile loading, PVB behaves as a hyperelastic material and material property is influenced by loading rate. Under dynamic loading the response of PVB is characterized by a time-dependent nonlinear elastic behavior. The ductility of PVB reduces as strain rate increases. The testing results are consistent with available testing data on PVB material at various strain rates. Analysis is made on the testing data to form strain-rate dependent stress–strain curves of PVB under tension