Cognition and pain share common neural substrates and interact reciprocally: chronic pain compromises cognitive performance, whereas cognitive processes modulate pain perception. In the present study, we established a...Cognition and pain share common neural substrates and interact reciprocally: chronic pain compromises cognitive performance, whereas cognitive processes modulate pain perception. In the present study, we established a non-drug-dependent rat model of context-based analgesia,where two different contexts(dark and bright) were matched with a high(52°C) or low(48°C) temperature in the hot-plate test during training. Before and after training,we set the temperature to the high level in both contexts.Rats showed longer paw licking latencies in trials with the context originally matched to a low temperature than those to a high temperature, indicating successful establishment of a context-based analgesic effect in rats. This effect was blocked by intraperitoneal injection of naloxone(an opioid receptor antagonist) before the probe. The context-based analgesic effect also disappeared after optogenetic activation or inhibition of the bilateral infralimbic or prelimbic sub-region of the prefrontal cortex. In brief, we established a context-based, non-drug dependent, placebo-like analgesia model in the rat. This model provides a new and useful tool for investigating the cognitive modulation of pain.展开更多
Currently,a growing number of educators are aware of the need to look for new approaches to replace the"spoon-feeding"method.Therefore,the context-based strategy began to emerge.This study aims to investigat...Currently,a growing number of educators are aware of the need to look for new approaches to replace the"spoon-feeding"method.Therefore,the context-based strategy began to emerge.This study aims to investigate how students derive information through contextual clues by examining the progress of Chinese middle school EFL students in terms of word recognition.The participants of this study included 20 eighth grade students from the same middle school.The participants sat for two different quizzes:a contextual vocabulary quiz(quiz A)and a direct instruction quiz(quiz B).In quiz A,the participants inferred the meaning of the target words from the example sentences,whereas in quiz B,the students utilized the accompanying English explanation to guess other new words.These students were in the experimental and control conditions,respectively.The two quizzes comprised of 15 multiple choice questions(MCQ)which differentiated the participants?word recognition response to two different learning methods.There were two significant findings from this study.First,the results showed that the context-based strategy leads to a better vocabulary learning performance compared to the direct instruction strategy.Second,although it is not as effective as the context-based strategy,the direct instruction strategy may assist EFL learners in remembering words in short term.展开更多
Traditional materials informatics leverages big data and machine learning(ML)to forecast material performance based on structural features but often overlooks valuable textual information.In this work,we proposed a no...Traditional materials informatics leverages big data and machine learning(ML)to forecast material performance based on structural features but often overlooks valuable textual information.In this work,we proposed a novel methodology for predicting material performance through context-based modeling using large language models(LLMs).This method integrates both numerical and textual information,enhancing predictive accuracy and scalability.In the case study,the approach is applied to predict the performance of solid amine CO_(2) adsorbents under direct air capture(DAC)conditions.ChatGPT 4o model was used to employ in-context learning to predict CO_(2) adsorption uptake based on input features,including material properties and experimental conditions.The results show that context-based modeling can reduce prediction error in comparison to traditional ML models in the prediction task.We adopted Sapley Additive exPlanations(SHAP)to further elucidate the importance of various input features.This work highlights the potential of LLMs in materials science,offering a cost-effective,efficient solution for complex predictive tasks.展开更多
In this paper, a Context-based 2D Variable Length Coding （C2DVLC） method for coding the transformed residuals in AVS video coding standard is presented. One feature in C2DVLC is the usage of multiple 2D-VLC tables a...In this paper, a Context-based 2D Variable Length Coding （C2DVLC） method for coding the transformed residuals in AVS video coding standard is presented. One feature in C2DVLC is the usage of multiple 2D-VLC tables and another feature is the usage of simple Exponential-Golomb codes. C2DVLC employs context-based adaptive multiple table coding to exploit the statistical correlation between DCT coefficients of one block for higher coding efficiency. ExpGolomb codes are applied to code the pairs of the run-length of zero coefficients and the nonzero coefficient for lower storage requirement. C2DVLC is a low complexity coder in terms of both computational time and memory requirement. The experimental results show that C2DVLC can gain 0.34dB in average for the tested videos when compared with the traditional 2D-VLC coding method like that used in MPEG-2. And compared with CAVLC in H.264/AVC, C2DVLC shows similar coding efficiency.展开更多
In opportunistic Networks,compromised nodes can attack social context-based routing protocols by publishing false social attributes information.To solve this problem,we propose a security scheme based on the identity-...In opportunistic Networks,compromised nodes can attack social context-based routing protocols by publishing false social attributes information.To solve this problem,we propose a security scheme based on the identity-based threshold signature which allows mobile nodes to jointly generate and distribute the secrets for social attributes in a totally self-organized way without the need of any centralized authority.New joining nodes can reconstruct their own social attribute signatures by getting enough partial signature services from encounter opportunities with the initial nodes.Mobile nodes need to testify whether the neighbors can provide valid attribute signatures for their routing advertisements in order to resist potential routing attacks.Simulation results show that:by implementing our security scheme,the network delivery probability of the social context-based routing protocol can be effectively improved when there are large numbers of compromised nodes in opportunistic networks.展开更多
Context-based adaptive binary arithmetic coding(CABAC) is the major entropy-coding algorithm employed in H.264/AVC.In this paper,we present a new VLSI architecture design for an H.264/AVC CABAC decoder,which optimizes...Context-based adaptive binary arithmetic coding(CABAC) is the major entropy-coding algorithm employed in H.264/AVC.In this paper,we present a new VLSI architecture design for an H.264/AVC CABAC decoder,which optimizes both decode decision and decode bypass engines for high throughput,and improves context model allocation for efficient external memory access.Based on the fact that the most possible symbol(MPS) branch is much simpler than the least possible symbol(LPS) branch,a newly organized decode decision engine consisting of two serially concatenated MPS branches and one LPS branch is proposed to achieve better parallelism at lower timing path cost.A look-ahead context index(ctxIdx) calculation mechanism is designed to provide the context model for the second MPS branch.A head-zero detector is proposed to improve the performance of the decode bypass engine according to UEGk encoding features.In addition,to lower the frequency of memory access,we reorganize the context models in external memory and use three circular buffers to cache the context models,neighboring information,and bit stream,respectively.A pre-fetching mechanism with a prediction scheme is adopted to load the corresponding content to a circular buffer to hide external memory latency.Experimental results show that our design can operate at 250 MHz with a 20.71k gate count in SMIC18 silicon technology,and that it achieves an average data decoding rate of 1.5 bins/cycle.展开更多
An adaptive pipelining scheme for H.264/AVC context-based adaptive binary arithmetic coding(CABAC) decoder for high definition(HD) applications is proposed to solve data hazard problems coming from the data dependenci...An adaptive pipelining scheme for H.264/AVC context-based adaptive binary arithmetic coding(CABAC) decoder for high definition(HD) applications is proposed to solve data hazard problems coming from the data dependencies in CABAC decoding process.An efficiency model of CABAC decoding pipeline is derived according to the analysis of a common pipeline.Based on that,several adaptive strategies are provided.The pipelining scheme with these strategies can be adaptive to different types of syntax elements(SEs) and the pipeline will not stall during decoding process when these strategies are adopted.In addition,the decoder proposed can fully support H.264/AVC high4:2:2 profile and the experimental results show that the efficiency of decoder is much higher than other architectures with one engine.Taking both performance and cost into consideration,our design makes a good tradeoff compared with other work and it is sufficient for HD real-time decoding.展开更多
Grid computing is concerned with the sharing and coordinated use of diverse resources in distributed “virtual organizations”. The heterogeneous, dynamic and multi-domain nature of these environments makes challengin...Grid computing is concerned with the sharing and coordinated use of diverse resources in distributed “virtual organizations”. The heterogeneous, dynamic and multi-domain nature of these environments makes challenging security issues that demand new technical approaches. Despite the recent advances in access control approaches applicable to Grid computing, there remain issues that impede the development of effective access control models for Grid applications. Among them there are the lack of context-based models for access control, and reliance on identity or capability-based access control schemes. An access control scheme that resolve these issues is presented, and a dynamically authorized role-based access control (D-RBAC) model extending the RBAC with context constraints is proposed. The D-RABC mechanisms dynamically grant permissions to users based on a set of contextual information collected from the system and user’s environments, while retaining the advantages of RBAC model. The implementation architecture of D-RBAC for the Grid application is also described.展开更多
Response features of mitral cells in the olfactory bulb were examined using principal component analysis to determine whether they contain information about odorant stimuli.Using microwire electrode array to record fr...Response features of mitral cells in the olfactory bulb were examined using principal component analysis to determine whether they contain information about odorant stimuli.Using microwire electrode array to record from the olfactory bulb in freely breathing anesthetized rats,we recorded responses of different mitral cells to saturated vapor of anisole(1 M),carvone(1 M),isobutanol(1 M),citral(1 M)and isoamyl actate(1 M).The responses of single mitral cells to the same odorant varied over time.The response profiles showed similarity during certain amount of period,which indicated that the response was not only depended on odor itself but also associated with context.Furthermore,the responses of single mitral cell to different odorants were observed with difference in firing rate.In order to recognize different odorant stimuli,we apply four cells as a sensing group for classification using principal component analysis.Features of each cell’s response describing both temporal and frequency characteristics were selected.The results showed that five different single molecular odorants can be distinguished from each other.These data suggest that action potentials of mitral cells may play a role in odor coding.展开更多
Recommender systems are rapidly transforming the digital world into intelligent information hubs.The valuable context information associated with the users’prior transactions has played a vital role in determining th...Recommender systems are rapidly transforming the digital world into intelligent information hubs.The valuable context information associated with the users’prior transactions has played a vital role in determining the user preferences for items or rating prediction.It has been a hot research topic in collaborative filtering-based recommender systems for the last two decades.This paper presents a novel Context Based Rating Prediction(CBRP)model with a unique similarity scoring estimation method.The proposed algorithm computes a context score for each candidate user to construct a similarity pool for the given subject user-item pair and intuitively choose the highly influential users to forecast the item ratings.The context scoring strategy has an inherent capability to incorporate multiple conditional factors to filter down the most relevant recommendations.Compared with traditional similarity estimation methods,CBRP makes it possible for the full use of neighboring collaborators’choice on various conditions.We conduct experiments on three publicly available datasets to evaluate our proposed method with random user-item pairs and got considerable improvement in prediction accuracy over the standard evaluation measures.Also,we evaluate prediction accuracy for every user-item pair in the system and the results show that our proposed framework has outperformed existing methods.展开更多
基金supported by grants from the National Natural Science Foundation of China (91732107, 31200835, 81571067, and 81521063)the National Basic Research Development Program (973 Program) of China (2014CB548200 and 2015CB554503)
文摘Cognition and pain share common neural substrates and interact reciprocally: chronic pain compromises cognitive performance, whereas cognitive processes modulate pain perception. In the present study, we established a non-drug-dependent rat model of context-based analgesia,where two different contexts(dark and bright) were matched with a high(52°C) or low(48°C) temperature in the hot-plate test during training. Before and after training,we set the temperature to the high level in both contexts.Rats showed longer paw licking latencies in trials with the context originally matched to a low temperature than those to a high temperature, indicating successful establishment of a context-based analgesic effect in rats. This effect was blocked by intraperitoneal injection of naloxone(an opioid receptor antagonist) before the probe. The context-based analgesic effect also disappeared after optogenetic activation or inhibition of the bilateral infralimbic or prelimbic sub-region of the prefrontal cortex. In brief, we established a context-based, non-drug dependent, placebo-like analgesia model in the rat. This model provides a new and useful tool for investigating the cognitive modulation of pain.
文摘Currently,a growing number of educators are aware of the need to look for new approaches to replace the"spoon-feeding"method.Therefore,the context-based strategy began to emerge.This study aims to investigate how students derive information through contextual clues by examining the progress of Chinese middle school EFL students in terms of word recognition.The participants of this study included 20 eighth grade students from the same middle school.The participants sat for two different quizzes:a contextual vocabulary quiz(quiz A)and a direct instruction quiz(quiz B).In quiz A,the participants inferred the meaning of the target words from the example sentences,whereas in quiz B,the students utilized the accompanying English explanation to guess other new words.These students were in the experimental and control conditions,respectively.The two quizzes comprised of 15 multiple choice questions(MCQ)which differentiated the participants?word recognition response to two different learning methods.There were two significant findings from this study.First,the results showed that the context-based strategy leads to a better vocabulary learning performance compared to the direct instruction strategy.Second,although it is not as effective as the context-based strategy,the direct instruction strategy may assist EFL learners in remembering words in short term.
基金supported by the Key Project of Natural Science Funds of Tianjin City(22JCZDJC00540).
文摘Traditional materials informatics leverages big data and machine learning(ML)to forecast material performance based on structural features but often overlooks valuable textual information.In this work,we proposed a novel methodology for predicting material performance through context-based modeling using large language models(LLMs).This method integrates both numerical and textual information,enhancing predictive accuracy and scalability.In the case study,the approach is applied to predict the performance of solid amine CO_(2) adsorbents under direct air capture(DAC)conditions.ChatGPT 4o model was used to employ in-context learning to predict CO_(2) adsorption uptake based on input features,including material properties and experimental conditions.The results show that context-based modeling can reduce prediction error in comparison to traditional ML models in the prediction task.We adopted Sapley Additive exPlanations(SHAP)to further elucidate the importance of various input features.This work highlights the potential of LLMs in materials science,offering a cost-effective,efficient solution for complex predictive tasks.
基金Supported by the National Natural Science Foundation of China under Grant No. 60333020 and the Natural Science Foundation of Beijing under Grant No. 4041003.
文摘In this paper, a Context-based 2D Variable Length Coding （C2DVLC） method for coding the transformed residuals in AVS video coding standard is presented. One feature in C2DVLC is the usage of multiple 2D-VLC tables and another feature is the usage of simple Exponential-Golomb codes. C2DVLC employs context-based adaptive multiple table coding to exploit the statistical correlation between DCT coefficients of one block for higher coding efficiency. ExpGolomb codes are applied to code the pairs of the run-length of zero coefficients and the nonzero coefficient for lower storage requirement. C2DVLC is a low complexity coder in terms of both computational time and memory requirement. The experimental results show that C2DVLC can gain 0.34dB in average for the tested videos when compared with the traditional 2D-VLC coding method like that used in MPEG-2. And compared with CAVLC in H.264/AVC, C2DVLC shows similar coding efficiency.
基金the Major national S&T program under Grant No. 2011ZX03005-002National Natural Science Foundation of China under Grant No. 60872041,61072066the Fundamental Research Funds for the Central Universities under Grant No. JY10000903001,JY10000901034
文摘In opportunistic Networks,compromised nodes can attack social context-based routing protocols by publishing false social attributes information.To solve this problem,we propose a security scheme based on the identity-based threshold signature which allows mobile nodes to jointly generate and distribute the secrets for social attributes in a totally self-organized way without the need of any centralized authority.New joining nodes can reconstruct their own social attribute signatures by getting enough partial signature services from encounter opportunities with the initial nodes.Mobile nodes need to testify whether the neighbors can provide valid attribute signatures for their routing advertisements in order to resist potential routing attacks.Simulation results show that:by implementing our security scheme,the network delivery probability of the social context-based routing protocol can be effectively improved when there are large numbers of compromised nodes in opportunistic networks.
基金Project supported by the National Natural Science Foundation of China(No.61100074)the Fundamental Research Funds for the Central Universities,China(No.2013QNA5008)
文摘Context-based adaptive binary arithmetic coding(CABAC) is the major entropy-coding algorithm employed in H.264/AVC.In this paper,we present a new VLSI architecture design for an H.264/AVC CABAC decoder,which optimizes both decode decision and decode bypass engines for high throughput,and improves context model allocation for efficient external memory access.Based on the fact that the most possible symbol(MPS) branch is much simpler than the least possible symbol(LPS) branch,a newly organized decode decision engine consisting of two serially concatenated MPS branches and one LPS branch is proposed to achieve better parallelism at lower timing path cost.A look-ahead context index(ctxIdx) calculation mechanism is designed to provide the context model for the second MPS branch.A head-zero detector is proposed to improve the performance of the decode bypass engine according to UEGk encoding features.In addition,to lower the frequency of memory access,we reorganize the context models in external memory and use three circular buffers to cache the context models,neighboring information,and bit stream,respectively.A pre-fetching mechanism with a prediction scheme is adopted to load the corresponding content to a circular buffer to hide external memory latency.Experimental results show that our design can operate at 250 MHz with a 20.71k gate count in SMIC18 silicon technology,and that it achieves an average data decoding rate of 1.5 bins/cycle.
基金Supported by the National Natural Science Foundation of China(No.61076021)the National Basic Research Program of China(No.2009CB320903)China Postdoctoral Science Foundation(No.2012M511364)
文摘An adaptive pipelining scheme for H.264/AVC context-based adaptive binary arithmetic coding(CABAC) decoder for high definition(HD) applications is proposed to solve data hazard problems coming from the data dependencies in CABAC decoding process.An efficiency model of CABAC decoding pipeline is derived according to the analysis of a common pipeline.Based on that,several adaptive strategies are provided.The pipelining scheme with these strategies can be adaptive to different types of syntax elements(SEs) and the pipeline will not stall during decoding process when these strategies are adopted.In addition,the decoder proposed can fully support H.264/AVC high4:2:2 profile and the experimental results show that the efficiency of decoder is much higher than other architectures with one engine.Taking both performance and cost into consideration,our design makes a good tradeoff compared with other work and it is sufficient for HD real-time decoding.
基金Supported by the National Natural Science Foundation of China (No.60403027) .
文摘Grid computing is concerned with the sharing and coordinated use of diverse resources in distributed “virtual organizations”. The heterogeneous, dynamic and multi-domain nature of these environments makes challenging security issues that demand new technical approaches. Despite the recent advances in access control approaches applicable to Grid computing, there remain issues that impede the development of effective access control models for Grid applications. Among them there are the lack of context-based models for access control, and reliance on identity or capability-based access control schemes. An access control scheme that resolve these issues is presented, and a dynamically authorized role-based access control (D-RBAC) model extending the RBAC with context constraints is proposed. The D-RABC mechanisms dynamically grant permissions to users based on a set of contextual information collected from the system and user’s environments, while retaining the advantages of RBAC model. The implementation architecture of D-RBAC for the Grid application is also described.
基金This research is supported by the National Natural Science Foundation of China(Grant 60725102).
文摘Response features of mitral cells in the olfactory bulb were examined using principal component analysis to determine whether they contain information about odorant stimuli.Using microwire electrode array to record from the olfactory bulb in freely breathing anesthetized rats,we recorded responses of different mitral cells to saturated vapor of anisole(1 M),carvone(1 M),isobutanol(1 M),citral(1 M)and isoamyl actate(1 M).The responses of single mitral cells to the same odorant varied over time.The response profiles showed similarity during certain amount of period,which indicated that the response was not only depended on odor itself but also associated with context.Furthermore,the responses of single mitral cell to different odorants were observed with difference in firing rate.In order to recognize different odorant stimuli,we apply four cells as a sensing group for classification using principal component analysis.Features of each cell’s response describing both temporal and frequency characteristics were selected.The results showed that five different single molecular odorants can be distinguished from each other.These data suggest that action potentials of mitral cells may play a role in odor coding.
基金This work is supported by National Natural Science Foundation of China(No.61672133)Sichuan Science and Technology Program(No.2019YFG0535)the 111 Project(No.B17008).
文摘Recommender systems are rapidly transforming the digital world into intelligent information hubs.The valuable context information associated with the users’prior transactions has played a vital role in determining the user preferences for items or rating prediction.It has been a hot research topic in collaborative filtering-based recommender systems for the last two decades.This paper presents a novel Context Based Rating Prediction(CBRP)model with a unique similarity scoring estimation method.The proposed algorithm computes a context score for each candidate user to construct a similarity pool for the given subject user-item pair and intuitively choose the highly influential users to forecast the item ratings.The context scoring strategy has an inherent capability to incorporate multiple conditional factors to filter down the most relevant recommendations.Compared with traditional similarity estimation methods,CBRP makes it possible for the full use of neighboring collaborators’choice on various conditions.We conduct experiments on three publicly available datasets to evaluate our proposed method with random user-item pairs and got considerable improvement in prediction accuracy over the standard evaluation measures.Also,we evaluate prediction accuracy for every user-item pair in the system and the results show that our proposed framework has outperformed existing methods.
