Behavioral and computational aspects of language and its acquisition

One of the greatest challenges facing the cognitive sciences is to explain what it means to know a language, and how the knowledge of language is acquired. The dominant approach to this challenge within linguistics has been to seek an efficient characterization of the wealth of documented structural properties of language in terms of a compact generative grammar—ideally, the minimal necessary set of innate, universal, exception-less, highly abstract rules that jointly generate all and only the observed phenomena and are common to all human languages. We review developmental, behavioral, and computational evidence that seems to favor an alternative view of language, according to which linguistic structures are generated by a large, open set of constructions of varying degrees of abstraction and complexity, which embody both form and meaning and are acquired through socially situated experience in a given language community, by probabilistic learning algorithms that resemble those at work in other cognitive modalities.     

Quantum Bayesian computation

Quantum Bayesian computation is an emerging field that levers the computational gains available from quantum computers. They promise to provide an exponential speed-up in Bayesian computation. Our article adds to the literature in three ways. First, we describe how quantum von Neumann measurement provides quantum versions of popular machine learning algorithms such as Markov chain Monte Carlo and deep learning that are fundamental to Bayesian learning. Second, we describe quantum data encoding methods needed to implement quantum machine learning including the counterparts to traditional feature extraction and kernel embeddings methods. Third, we show how quantum algorithms naturally calculate Bayesian quantities of interest such as posterior distributions and marginal likelihoods. Our goal then is to show how quantum algorithms solve statistical machine learning problems. On the theoretical side, we provide quantum versions of high dimensional regression, Gaussian processes and stochastic gradient descent. On the empirical side, we apply a quantum FFT algorithm to Chicago house price data. Finally, we conclude with directions for future research.     

Long-term depression and other synaptic plasticity in the cerebellum

Cerebellar long-term depression (LTD) is a type of synaptic plasticity and has been considered as a critical cellular mechanism for motor learning. LTD occurs at excitatory synapses between parallel fibers and a Purkinje cell in the cerebellar cortex, and is expressed as reduced responsiveness to transmitter glutamate. Molecular induction mechanism of LTD has been intensively studied using culture and slice preparations, which has revealed critical roles of Ca²⁺, protein kinase C and endocytosis of AMPA-type glutamate receptors. Involvement of a large number of additional molecules has also been demonstrated, and their interactions relevant to LTD mechanisms have been studied. In vivo experiments including those on mutant mice, have reported good correlation of LTD and motor learning. However, motor learning could occur with impaired LTD. A possibility that cerebellar synaptic plasticity other than LTD compensates for the defective LTD has been proposed.     

Approximate time-optimal control of quantum ensembles based on sampling and learning

For a general quantum ensemble with Hamiltonian fluctuations, this paper proposes a sampling-based two-stage approximate time-optimal control algorithm with momentum terms and achieves a high-fidelity state transition of all member systems to a common target state within an approximate minimum time. The fidelity and the control time are respectively optimized in the two stages. In particular, the introduction of momentum terms greatly improves the convergence rate of the algorithm. Simulation experiments on a two-level quantum ensemble verify the effectiveness of the proposed algorithm.     

基于自编码网络流形学习的毛竹笋不溶性膳食纤维含量红外光谱建模

提出了一种结合自编码网络(AN)流形学习和偏最小二乘(PLS)法的红外光谱建模方法AN-PLS。AN-PLS方法首先用AN算法对红外光谱数据进行非线性降维,再结合PLS建立回归模型。利用该方法建立了毛竹笋中不溶性膳食纤维含量的近红外光谱和中红外光谱回归模型。结果表明,用AN-PLS方法建立的回归模型,比用其他常用光谱数据预处理方法结合PLS及用单独PLS算法建立的模型具有更小的预测均方根误差RMSEP和更高的决定系数R2,因此,AN-PLS具有较优的建模与预测能力,利用近红外光谱和中红外光谱技术结合AN-PLS建模,可实现毛竹笋中不溶性膳食纤维含量的准确测量。     

优化设计性实验教学培养学生的科学素质

通过论证设计性实验与科学素质之间的关系，提出体现培养科学素质的问题式学习模式，并在大学物理实验教学中进行了教学改革试验。     

基于自适应核学习相关向量机的乳腺X线图像微钙化点簇处理方法研究

采用自适应核学习相关向量机方法, 结合形态学滤波和Kallergi分簇标准, 研究了乳腺X线图像中微钙化点簇的处理. 首先将微钙化点检测看作一个监督学习问题, 然后应用自适应核学习相关向量机作为分类器判断图像中每一个位置是否为微钙化点并采用形态学处理滤除干扰噪声, 最后对获得的微钙化点采用Kallergi标准进行分簇. 为提高运算速度, 在微钙化点检测时将整个图像分解为多个子图像并行运算, 实现了一种基于自适应核学习相关向量机的微钙化点簇快速处理方法. 实验结果和分析表明, 自适应核学习相关向量机方法算法性能优于相关向量机方法, 特别是实现的快速方法能进一步降低微钙化点簇的处理时间. 关键词： 乳腺X线图像 微钙化点簇 相关向量机 自适应核学习     

Chaos in social learning with multiple true states

Most existing social learning models assume that there is only one underlying true state. In this work, we consider a social learning model with multiple true states, in which agents in different groups receive different signal sequences generated by their corresponding underlying true states. Each agent updates his belief by combining his rational self-adjustment based on the external signals he received and the influence of his neighbors according to their communication. We observe chaotic oscillation in the belief evolution, which implies that neither true state could be learnt correctly by calculating the largest Lyapunov exponents and Hurst exponents.     

高光谱图像降维的判别流形学习方法

本文提出了一种高光谱图像降维的判别流形学习方法.针对获取的大量遥感对地观测数据存在大量冗余信息的特点,引入改进的流形学习方法对高光谱遥感数据进行降维处理,以提高遥感图像自动分类的总体准确度.该方法充分利用遥感图像自动分类中训练样本的判别信息,将输入样本的类别信息加入到常规流形学习方法的框架中,从本质上提高输出的特征在低维空间中的判别力.同时,引入线性化模型以解决流形学习方法中常见的小样本问题.对高光谱遥感图像自动分类的实验表明,基于判别流形学习的高光谱遥感图像自动分类方法能够显著地提高图像分类准确度.     

基于RBF神经网络的磨矿粒度软测量模型及实现

磨矿分级作业是选矿生产中的重要环节,磨矿粒度的好坏直接影响浮选的精矿品位和尾矿回收率;在实际生产中粒度的测量有在线粒度分析仪,但存在成本高、维修率高,离线实验室化验又有时间延迟大的问题;对实际磨矿分级作业过程进行了分析,提出用径向基函数(RBF)神经网络建立磨矿粒度软测量模型,采用正交最小二乘法(OLS)算法对网络进行训练学习,泛化校验;仿真结果表明,在较少训练数据下该网络非线性处理能力和逼近能力依然很强,学习时间短,模型基本符合要求;通过OPC技术将Matlab与PKS控制系统相结合,实现实时软测量磨矿粒度。