Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While suc...Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.展开更多
<div style="text-align:justify;"> Knowledge tracking model has been a research hotspot in the field of educational data mining for a long time. Knowledge tracking can automatically discover students’ ...<div style="text-align:justify;"> Knowledge tracking model has been a research hotspot in the field of educational data mining for a long time. Knowledge tracking can automatically discover students’ weak knowledge points, which helps to improve students’ self-motivation in learning and realize personalized guidance. The existing KT model has some shortcomings, such as the limitation of the calculation of knowledge growth, and the imperfect forgetting mechanism of the model. To this end, we proposed a new knowledge tracking model based on learning process (LPKT), LPKT applies the idea of Memory Augmented Neural Net-work(MANN).When we model the learning process of students, two additional important factors are considered. One is to consider the current state of knowledge of the students when updating the dynamic matrix of the neural network, and the other is to improve the forgetting mechanism of the model. In this paper we verified the effectiveness and superiority of LPKT through comparative experiments, and proved that the model can improve the effect of knowledge tracking and make the process of deep knowledge tracking easier to understand. </div>展开更多
基金National Natural Science Foundation of China(62171305,62405206,62004135,62001317,62111530301)Natural Science Foundation of Jiangsu Province(BK20240778,BK20241917)+3 种基金State Key Laboratory of Advanced Optical Communication Systems and Networks,China(2023GZKF08)China Postdoctoral Science Foundation(2024M752314)Postdoctoral Fellowship Program of CPSF(GZC20231883)Innovative and Entrepreneurial Talent Program of Jiangsu Province(JSSCRC2021527).
文摘Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.
文摘<div style="text-align:justify;"> Knowledge tracking model has been a research hotspot in the field of educational data mining for a long time. Knowledge tracking can automatically discover students’ weak knowledge points, which helps to improve students’ self-motivation in learning and realize personalized guidance. The existing KT model has some shortcomings, such as the limitation of the calculation of knowledge growth, and the imperfect forgetting mechanism of the model. To this end, we proposed a new knowledge tracking model based on learning process (LPKT), LPKT applies the idea of Memory Augmented Neural Net-work(MANN).When we model the learning process of students, two additional important factors are considered. One is to consider the current state of knowledge of the students when updating the dynamic matrix of the neural network, and the other is to improve the forgetting mechanism of the model. In this paper we verified the effectiveness and superiority of LPKT through comparative experiments, and proved that the model can improve the effect of knowledge tracking and make the process of deep knowledge tracking easier to understand. </div>
