严羽《沧浪诗话》“入神”说探析

严羽在《沧浪诗话》中提出“入神”的诗学观点,它统一于严羽的诗学系统中,与其他的诗学观点有着千丝万缕的联系。“入神”继承了“神”字在文学(诗学)观念的应用,是严羽提出来的“诗之极致”。在严羽看来,能够达到“入神”境界只有李白与杜甫。严羽将李、杜作为“入神”的代表,暗合了中国古典诗歌审美特征中现实之风和浪漫之风的发展,囊括了古代诗歌从先秦到唐宋的发展历程。

“入神”方可长精神

有种说法，读经典可以使人长精神。此话 不虚。但有个前提，就是读经典必须“入神”，如此方可长精神。入神者，一心一意、精神贯注之谓也。唯有一心一意，读起经典来才有可能忘味九成；唯有精神贯注，读经典的过程才有可能使书中之精义渗透到自己脑海中，继而生发出新的精神来。这个过程，也就是古人讲的：“腹有诗书气自华。”

入境 入情 入神——小学语文苏教版五年级上册《天火之谜》教学例谈

有人说课堂不是闲庭信步，是带着孩子爬山。爬什么山？知识之山，情感之山，思维之山。我以为此言极是。当教师带领孩子气喘吁吁、大汗淋漓登上峰顶时，便会有山高我为峰的感受。“入境”、“入情”、“入神”就是语文课堂爬山的过程。从入境到入情，从入情到入神是一个层层递进、

严羽《沧浪诗话》“入神”说探赜——以宋代佛教及书论、画论为视角

严羽在《沧浪诗话》中提出"入神"说,以之为诗之极致。"入神"说系经过历时的传承与共时的演变而形成。"入神"一词由《周易》"精义入神"而来,其内涵则在唐宋之时得以扩大。宋代佛典常以"入神"来形容悟性高绝、佛法精深的僧人形象,赋予了"入神"以注重体悟与超越文字的内涵。宋代书论则以超越律度的"入神"为极致,这与画论中所言"造理入神,迥得天意""天机所到,学者不及"的境界相一致。以宋代佛教及书论、画论中的"入神"审视严羽的"入神"说,便可知晓其"入神"是指超越了具体的诗之法、诗之品、诗之用工等律度,不借文字而能剖出肺腑的诗歌创作之境。

A Hybrid Compensation Scheme for the Input Rate-Dependent Hysteresis of the Piezoelectric Ceramic Actuators

A hybrid compensation scheme for piezoelectric ceramic actuators(PEAs)is proposed.In the hybrid compensation scheme,the input rate-dependent hysteresis characteristics of the PEAs are compensated.The feedforward controller is a novel input rate-dependent neural network hysteresis inverse model,while the feedback controller is a proportion integration differentiation(PID)controller.In the proposed inverse model,an input ratedependent auxiliary inverse operator(RAIO)and output of the hysteresis construct the expanded input space(EIS)of the inverse model which transforms the hysteresis inverse with multi-valued mapping into single-valued mapping,and the wiping-out,rate-dependent and continuous properties of the RAIO are analyzed in theories.Based on the EIS method,a hysteresis neural network inverse model,namely the dynamic back propagation neural network(DBPNN)model,is established.Moreover,a hybrid compensation scheme for the PEAs is designed to compensate for the hysteresis.Finally,the proposed method,the conventional PID controller and the hybrid controller with the modified input rate-dependent Prandtl-Ishlinskii(MRPI)model are all applied in the experimental platform.Experimental results show that the proposed method has obvious superiorities in the performance of the system.

审美意象的心理生成

"意象"是中国古代诗学中的一个重要概念,其内涵是人与自然的审美统一,表现为"意"与"象"、"物"与"我"的统一。人们对"意象"的认识,只注重禅佛、《周易》、玄学的影响,却忽略了意象的生成机制。意象的心理生成包含"虚心"、"化我"、"入神"三个阶段。

Behavioral modeling of RF power amplifiers with time-delay feed-forward neural networks

A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the parameters, the back- propagation algorithm is applied to train the proposed neural networks. The proposed model is verified by the typical odd- order-only memory polynomial model in simulation, and the performance is compared with different numbers of taped delay lines（TDLs） and perceptrons of the hidden layer. For validating the TDFFNN model by experiments, a digital test bench is set up to collect input and output data of power amplifiers at a 60 × 10^6 sample/s sampling rate. The 3.75 MHz 16-QAM signal generated in the vector signal generator（VSG） is chosen as the input signal, when measuring the dynamic AM/AM and AM/PM characteristics of power amplifiers. By comparisons and analyses, the presented model provides a good performance in convergence, accuracy and efficiency, which is approved by simulation results and experimental results in the time domain and frequency domain.

要览

解读“用正确的方式处理人际关系”的理论视点及其教育意义 ；论教师实践知识与实践教育；建安风骨的发展历程；严羽诗学“入神”说试析；宗白华美学诗性品格解读。

Hyperpolarization-activated cyclic nucleotide-gated cation channel subtypes differentially modulate the excitability of murine small intestinal afferents

AIM： To assess the role of hyperpolarization-activated cyclic nucleotide-gated cation （HCN） channels in regu- lating the excitability of vagal and spinal gut afferents. METHODS： The mechanosensory response of mesen- teric afferent activity was measured in an ex vivo murine jejunum preparation. HCN channel activity was recorded through voltage and current clamp in acutely dissoci- ated dorsal root ganglia （DRG） and nodose ganglia （NG） neurons retrogradely labeled from the small intestine through injection of a fluorescent marker （DiI）. The isoforms of HCN channels expressed in DRG and NG neurons were examined by immunohistochemistry. RESULTS： Ramp distension of the small intestine evok- ed biphasic increases in the afferent nerve activity, re- flecting the activation of low- and high-threshold fibers.HCN blocker CsCl （5 mmol/L） preferentially inhibited the responses of low-threshold fibers to distension and showed no significant effects on the high-threshold re- sponses. The effect of CsCI was mimicked by the more selective HCN blocker ZD7288 （10 ~mol/L）. In 71.4% of DiI labeled DRG neurons （/7 = 20） and 90.9% of DiI labeled NG neurons （n = 10）, an inward current （Ih current） was evoked by hyperpolarization pulses which was fully eliminated by extracellular CsCI. In neurons expressing Ih current, a typical ＂sag＂ was observed upon injection of hyperpolarizing current pulses in cur- rent-clamp recordings. CsCI abolished the sag entirely. In some DiI labeled DRG neurons, the Ih current was potentiated by 8-Br-cAMP, which had no effect on the Ih current of DiI labeled NG neurons. Immunohistochem- istry revealed differential expression of HCN isoforms in vagal and spinal afferents, and HCN2 and HCN3 seemed to be the dominant isoform in DRG and NG, respec- tively.CONCLUSION： HCNs differentially regulate the excit- ability of vagal and spinal afferent of murine small in- testine.

Dimensionality Reduction with Input Training Neural Network and Its Application in Chemical Process Modelling

Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.

A NOVEL INTRUSION DETECTION MODE BASED ON UNDERSTANDABLE NEURAL NETWORK TREES

Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.

Video-assisted Endoscopic Thyroidectomy by the Breast Approach

Objective: To retrospectively evaluate the feasibility and clinical value of video assisted endoscopic thyroidectomy by the breast approach. Methods: From December 2002 to May 2003, 28 patients with a mean age of 28 years (range from 20 to 45 years) were selected and given video assisted endoscopic thyroidectomy by the breast approach. The subcutaneous space in the breast area and the subplatysmal space in the neck were bluntly dissociated through a 10 mm incision between the nipples, and CO 2 was insufflated at 6 8 kban to create the operative space. Three trocars were inserted in the mammary regions, and dissection of the thyroid and division of the thyroid vessels and parenchyma were performed endoscopically using an ultrasonically activated scalpel. The recurrent laryngeal nerve, the superior laryngeal nerve, and the parathyroid glands were preserved properly. Results: Among the patients, 3 were mass resections, 17 subtotal lobectomies, 2 total lobectomies, and 6 subtotal lobectomies plus contralateral mass resections. The mean operative time was (87.1±26.0) min; the mean estimated blood loss was (47.9±19.6) ml; and the mean postoperative hospital stay was (3.4±0.7) d. The drainage tubes were pulled out at 36 60 h postoperatively. There were no conversions to open surgery or complications. No scars left in the neck, and the patients were satisfied with the postoperative appearance. Conclusion: Video assisted endoscopic thyroidectomy using a breast approach and low pressure subcutaneous CO 2 insufflation is a feasible and safe procedure, which results in satisfactory appearance. We believe that video assisted endoscopic thyroidectomy by such approach will play a role in the future.

Experience of Cervical Plexus Reinnervation for Patients with Unilateral Recurrent Laryngeal Nerve Invasion or Injury

OBJECTIVE To explore the clinical and therapeutic effects of cervical plexus reinnervation for infiltrated or injured unilateral recurrent laryngeal nerve （URLN）. METHODS Functional neck dissection for removal of differentiated thyroid carcinoma （DTC） in patients was performed, in which cervical plexus reinnervation was adopted for patients with stage I disease and URLN with injury or with tumor invasion. Outcomes of surgery were evaluated by examination under fibrolaryngoscope, and the patients＇ voices were evaluated before and after surgery. RESULTS All cases were followed up for 3 mon-2 years （average 8 mon）. Abductory motion of the vocal cords of 15 patients was completely or partly restored, but 3 patients＇ vocal cords were immovable. The recovery rate of abductory motion of the paralyzed vocal cords was 83.33% （15/18）. The function of phonation in the 16 patients was restored to normal or near normal limits, and their hoarseness was improved significantly. CONCLUSION Cervical plexus-URLN reinnervation should be considered when treating patients with unilateral vocal cord paralysis. Removing the tumor simultaneously with cervical plexus reinnervation during surgery for repair of unilateral recurrent laryngeal nerve injury was convenient and easy to perform with less functional damage compared with other methods of reinnervation. The abductory motion of vocal cord could be satisfactorily restored by this reinnervation. Surgical performance skills and application of neurotrophic drugs were important for the success of the surgery.

Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network

To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network(AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts: groups of subnets based on well trained Autoassociative Neural Networks(AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method,the subnets are designed according to the classification of the data attributes. For getting the classification, an effective method called Extension Data Attributes Classification(EDAC) is adopted. Soft sensor using AHNN based on EDAC(EDAC-AHNN) is introduced. As a case study, the production data of Purified Terephthalic Acid(PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature, which shows the efficiency of the proposed model.

Efficient Selection of Linearly Independent Atomic Features for Accurate Machine Learning Potentials

Machine learning potentials are promising in atomistic simulations due to their comparable accuracy to first-principles theory but much lower computational cost.However,the reliability,speed,and transferability of atomistic machine learning potentials depend strongly on the way atomic configurations are represented.A wise choice of descriptors used as input for the machine learning program is the key for a successful machine learning representation.Here we develop a simple and efficient strategy to automatically select an optimal set of linearly-independent atomic features out of a large pool of candidates,based on the correlations that are intrinsic to the training data.Through applications to the construction of embedded atom neural network potentials for several benchmark molecules with less redundant linearly-independent embedded density descriptors,we demonstrate the efficiency and accuracy of this new strategy.The proposed algorithm can greatly simplify the initial selection of atomic features and vastly improve the performance of the atomistic machine learning potentials.

Extending self-organizing maps for supervised classification of remotely sensed data

An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.

Submesoscale activity over the shelf of the northern South China Sea in summer:simulation with an embedded model

We applied a primitive equation ocean model to simulate submesoscale activities and processes over the shelf of the northern South China Sea(NSCS) with a one-way nesting technology for downscaling.The temperature and density fields showed that submesoscale activities were ubiquitous in the NSCS shelf.The vertical velocity was considerably enhanced in submesoscale processes and could reach an average of 58 m per day in the subsurface.At this point,the mixed layer depth also was deepened along the front,and the surface kinetic energy also increased with the intense vertical movement induced by submesoscale activity.Thus,submesoscale stirring/mixing is important for tracers,such as temperature,salinity,nutrients,dissolved organic,and inorganic carbon.This result may have implication for climate and biogeochemical investigations.

On Contemplative Psychology

This paper is a revised version of a seminal paper, written as early as 1986, that introduces the concept of contemplative psychology as a psychology that is an intrinsic part of the contemplative traditions of most world religions. It refers to the psychological insights and methods that are-often implicitly-present in the spiritual traditions themselves. The paper delineates this psychology as a psychology in its own right and in dialogue with the conventional view of psychology and science. Later research by the author has been published in two books entitled Contemplative Psychology and The Spiritual Path： An Introduction to the Psychology of the Spiritual Traditions.

Three-dimensional neural network tracking control of autonomous underwater vehicles with input saturation

This paper addresses the problem of three-dimensional trajectory tracking control for underactuated autonomous underwater vehicles in the presence of parametric uncertainties,environmental disturbances and input saturation.First,a virtual guidance control strategy is established on the basis of tracking error kinematics,which resolves the overall control system into two cascade subsystems.Then,a first-order sliding mode differentiator is introduced in the derivation to avoid tedious analytic calculation,and a Gaussian error function-based continuous differentiable symmetric saturation model is explored to tackle the issue of input saturation.Combined with backstepping design techniques,the neural network control method and an adaptive control approach are used to estimate composite items of the unknown uncertainty and approximation errors.Meanwhile,Lyapunov-based stability analysis guarantees that control error signals of the closed-loop system are uniformly ultimately bounded.Finally,simulation studies are conducted for the trajectory tracking of a moving target and a spiral line to validate the effectiveness of the proposed controller.

China’s Gini Coefficient:Myths and Realities

Using retroactive adjustment approach of history data published by the National Bureau of Statistics （NBS）, this study has adjusted micro-level survey data of China Household Income Project Survey （CHIPS, 2007） and conducted point estimation on household income Gini coefficient using the NBS method. On this basis, the standard error of the point estimation of China＇s Gini coefficient is estimated using bootstrap method, creating a confidence interval of Gini coefficient. Results indicate that among five continuous declines of Gini coefficient between 2008 and 2013, only three declines are statistically significant. It is thus too early to jump at the conclusion that the Gini coefficient of China＇s household income distribution has already entered into a downward channel and at least the argument that China＇s Gini coefficient has been on the decline for five consecutive years is questionable.