Extract Executable Action Sequences from Natural Language Instructions Based on DQN for Medical Service Robots

The emergence and popularization of medical robots bring great convenience to doctors in treating patients. The core of medical robots is the interaction and cooperation between doctors and robots, so it is crucial to design a simple and stable human-robots interaction system for medical robots. Language is the most convenient way for people to communicate with each other, so in this paper, a DQN agent based on long-short term memory (LSTM) and attention mechanism is proposed to enable the robots to extract executable action sequences from doctors’ natural language instructions. For this, our agent should be able to complete two related tasks: 1) extracting action names from instructions. 2) extracting action arguments according to the extracted action names. We evaluate our agent on three datasets composed of texts with an average length of 49.95, 209.34, 417.17 words respectively. The results show that our agent can perform better than similar agents. And our agent has a better ability to handle long texts than previous works

Modulating Effects of Contextual Emotions on the Neural Plasticity Induced by Word Learning

Recently, numerous studies have investigated the neurocognitive mechanism of learning words in isolation or in semantic contexts. However, emotion as an important influencing factor on novel word learning has not been fully considered in the previous studies. In addition, the effects of emotion on word learning and the underlying neural mechanism have not been systematically investigated. Sixteen participants were trained to learn novel concrete or abstract words under negative, neutral, and positive contextual emotions over 3 days; then, fMRI scanning was done during the testing sessions on day 1 and day 3. We compared the brain activations in day 1 and day 3 to investigate the role of contextual emotions in learning different types of words and the corresponding neural plasticity changes. Behaviorally, the performance of the words learned in the negative context was lower than those in the neutral and positive contexts, which indicated that contextual emotions had a significant impact on novel word learning. Correspondingly, the functional plasticity changes of the right angular gyrus (AG), bilateral insula, and anterior cingulate cortex (ACC) induced by word learning were modulated by the contextual emotions. The insula also was sensitive to the concreteness of the learned words. More importantly, the functional plasticity changes of the left inferior frontal gyrus (left IFG) and left fusiform gyrus (left FG) were interactively influenced by the contextual emotions and concreteness, suggesting that the contextual emotional information had a discriminable effect on different types of words in the neural mechanism level. These results demonstrate that emotional information in contexts is inevitably involved in word learning. The role of contextual emotions in brain plasticity for learning is discussed

Two-Stream Retentive Long Short-Term Memory Network for Dense Action Anticipation

Analyzing and understanding human actions in long-range videos has promising applications, such as video surveillance, automatic driving, and efficient human-computer interaction. Most researches focus on short-range videos that predict a single action in an ongoing video or forecast an action several seconds earlier before it occurs. In this work, a novel method is proposed to forecast a series of actions and their durations after observing a partial video. This method extracts features from both frame sequences and label sequences. A retentive memory module is introduced to richly extract features at salient time steps and pivotal channels. Extensive experiments are conducted on the Breakfast data set and 50 Salads data set. Compared to the state-of-the-art methods, the method achieves comparable performance in most cases

The oyster genome reveals stress adaptation and complexity of shell formation

The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa. © 2012 Macmillan Publishers Limited. All rights reserved

On the efficiency of resources utilization in strategic peer-to-peer systems

Peer-to-peer (P2P) systems have achieved outstanding success during the past decades and much efforts has been put into investigating incentive mechanisms for strategic P2P networks. In the numerous studies of P2P system, efficient resource utilization has always been a critical factor in designing incentive schemes. Most existing studies try to encourage strategic peers to contribute more to the system, in order to maximize the resources. However, without considering how to effectively measure contributions and without adopting well-designed trading policies, simply motivating more contributions could lead to outcomes that do not match the original intention. This thesis, therefore, focuses on the investigation of efficient resource utilization in strategic P2P systems. First, it is found that increased contributions in terms of upload rate does not necessarily lead to better system performance. Observing that different chunks have different values to both the system and individual peers, a value-based metric is devised to measure contributions instead of using rate-based metrics. A variation of BitTorrent is also proposed, called value-based BitTorrent (VBT). VBT is found to effectively punish the strategic behaviors of an underreporting chunk map, and there is a positive correlation between investment and return for cooperative peers in VBT networks. Moreover, VBT always outperforms BitTorrent in terms of system performance. Second, taking the chunk value in the reciprocity process into consideration, the overpayment problem in a BitTorrent network is investigated, and four side effects of overpayment are identified. A new scheme is proposed to visualize overpayment and a series of metrics is proposed based on this method to quantify overpayment. The proposed value-based approach is found to be able to alleviate the degree of overpayment and consequently relieve the side effects of overpayment. Third, the performance of popular protocols in a P2P file-sharing system is investigated from the perspective of overpayment. These protocols are studied in two directions, when the measurement metrics are varied and when trading policy becomes tighter. The correlations between fairness, performance, and robustness are also examined. Finally, because overpayment is a fundamental problem of improper price setting, and auction is a widely used and effective method in setting prices in distributed systems. Auction is analytically proved that it is able to lead to optimal price without overpayment. However, most existing auction schemes are based on credit, which could induce a huge overhead in maintaining a monetary system, and monetary systems also have many inherent problems, such as inflation. The efficacy of applying an auction-like approach in P2P systems without money is investigated. A simplified version of the barter-based auction-like approach is tested in P2P file sharing and its overpayment degree is evaluated. Moreover, a novel barter-based auction-like approach is proposed for a P2P streaming system, and it is found that it can successfully punish strategic behaviors, with overall system performance outperforming a tit-for-tat strategy.published_or_final_versionElectrical and Electronic EngineeringDoctoralDoctor of Philosoph

Modulating Effects of Contextual Emotions on the Neural Plasticity Induced by Word Learning.

Recently, numerous studies have investigated the neurocognitive mechanism of learning words in isolation or in semantic contexts. However, emotion as an important influencing factor on novel word learning has not been fully considered in the previous studies. In addition, the effects of emotion on word learning and the underlying neural mechanism have not been systematically investigated. Sixteen participants were trained to learn novel concrete or abstract words under negative, neutral, and positive contextual emotions over 3 days; then, fMRI scanning was done during the testing sessions on day 1 and day 3. We compared the brain activations in day 1 and day 3 to investigate the role of contextual emotions in learning different types of words and the corresponding neural plasticity changes. Behaviorally, the performance of the words learned in the negative context was lower than those in the neutral and positive contexts, which indicated that contextual emotions had a significant impact on novel word learning. Correspondingly, the functional plasticity changes of the right angular gyrus (AG), bilateral insula, and anterior cingulate cortex (ACC) induced by word learning were modulated by the contextual emotions. The insula also was sensitive to the concreteness of the learned words. More importantly, the functional plasticity changes of the left inferior frontal gyrus (left IFG) and left fusiform gyrus (left FG) were interactively influenced by the contextual emotions and concreteness, suggesting that the contextual emotional information had a discriminable effect on different types of words in the neural mechanism level. These results demonstrate that emotional information in contexts is inevitably involved in word learning. The role of contextual emotions in brain plasticity for learning is discussed

Strain-Sensing Mechanism and Axial Stress Response Characterization of Bolt Based on Fiber Bragg Grating Sensing

The anchoring quality of bolts is related to roadway safety and the surrounding rock stability. Due to the limitations of conventional monitoring methods in capturing strain, there still exists a gap in the real-time perception of the mechanical properties of bolts at the micro-scale. This paper proposes a new approach to detecting bolts’ anchoring qualities based on the fiber Bragg grating sensing principle. Moreover, it studies the strain transmission mechanism between the surface-bonded fiber Bragg grating and the bolt. A fiber-optic monitoring test platform of anchor bolt anchoring quality is built. The full-length anchor bolt’s strain evolution law and axial force distribution characteristics are studied during the pull-out test. The study results have shown that the theoretical value of the fiber strain transfer coefficient can be used to calculate the strain of the bolt. The bolt pull-out test verified the accuracy of using the fiber Bragg grating bolt axial force characterization equation to estimate the bolt stress. On the other hand, the correlation between the bolt axial force and the fiber Bragg grating monitoring value follows an exponential pattern. This study provides an important basis for improving the understanding of a bolt anchoring mechanism and the stability control of a roadway’s surrounding rock