神经法学视角下论司法公信力与司法评估

自媒体时代,呈现出“司法民意”很大程度上影响审判的现象,究其核心,背后反映的是司法公信力问题。司法公信力是司法机关与社会民众双向互动互评、“信任关系”发展的动态过程。司法评估对我国司法公信力与司法文明建设具有重要影响,但目前我国还需要更准确性、客观性、科学性的鉴定评估技术与评估方法。近年来,神经法学这一交叉学科的兴起,提供大量的客观、科学的神经生物学证据,协助司法机关更科学化、专业化地进行司法评估、司法精神鉴定、刑事责任能力的判定、罪犯再犯风险、罪犯人身危险性预测、罪犯刑事处遇、罪犯定罪与量刑等问题,不仅对于罪犯循证矫正实践具有重要影响,一定程度上也对司法公正、司法权威与司法公信力等具有重要建设性作用。但目前,神经法学的研究刚刚起步,还需要神经法学研究者们不断努力与深入研究,才能为法学理论、司法评估实践与司法公信力等做出贡献。

青少年脑发育的神经法学研究与应用

神经科学研究的发展推动并孕育了新兴交叉学科——神经法学,青少年脑发育与其刑事责任能力的关系是神经法学的重要关注内容。研究发现,青少年中期冒险行为倾向加剧可能源于其社会情感和认知控制的脑发育轨迹不同,由皮层下组织主导的社会情感系统呈倒U形发展趋势,在青少年中期达到峰值,而由额叶成熟主导的认知控制系统发展呈线性增长,到成人早期才完成,即,皮层下组织与前额皮层的功能成熟不平衡导致了青少年的高犯罪率。这些神经科学证据已经进入美国青少年刑事案件的审判并产生实质影响,但也有人对神经科学证据应用于法庭持有异议。相对于法律系统的需求,青少年脑发育的神经法学需要在研究和实践方面同时努力。

神经法学:年轻的认知神经科学与古老的法学联姻

神经法学是新兴的交叉学科,它是用认知神经科学方法研究与法律有关心理与行为的神经机制,并探讨法律系统如何应对认知神经科学发展所带来的问题,主要包括:(1)如何检验当事人或证人是否说谎和是否存在错误记忆等;(2)探讨认知神经科学成果对刑事司法实践和刑事责任理论可能构成的影响;(3)对某些特殊个体未来行为的危险性作出预测,以便辅助司法实践;(4)认知操控药物与技术应用的法律管制问题.本文从上述4个方面对神经法学进行了详细介绍.神经法学的研究不仅加深了对法律行为背后心理和神经机制的理解,也对司法实践产生影响.但刚刚诞生的神经法学目前需处理好研究的中国化等问题.

刑事责任能力评定的神经法学观点

认知神经科学对法律行为背后神经心理机制的聚焦和讨论,催生了神经科学与法学的联姻—神经法学。其中,从认知神经科学的角度来评定刑事责任能力是神经法学的重要研究领域。文章重点介绍了精神疾病患者决策过程的认知神经科学研究成果和反社会个体的脑结构和功能异常以及基因多态性的研究证据,来分析讨论这两个特殊群体的刑事责任能力,以此说明认知神经科学可能对司法实践中刑事责任能力评定带来的影响。

神经法学视野中的道德教育

神经法学是认知神经科学与法学联姻的新兴交叉学科,它是用认知神经科学的技术手段研究法律相关的心理行为的认知神经机制。脑科学与法学的结合,必将有助于进一步了解人类道德与不道德、守法与违法行为的认知神经机制。神经法学的研究不仅有助于人们理解违法行为的认知神经机制及其防御途径,而且对道德教育也具有重要的意义和启示。因此,未来的道德教育应该有选择地吸收和借鉴神经法学的相关成果。

计量法学、计算法学到认知法学的演进

本文从技术发展和学科交叉的角度,论述了一般量化方法、计算智能和认知智能先后被应用于法学系统对法学理论与司法实践产生的重要推动作用,首次提出了认知法学的理论和概念,分析了认知法学的内容、研究意义与学科分支,并梳理了计量法学、计算法学与认知法学的发展脉络.自然科学的方法引入法律领域对减少法律系统中的主观性、为法律中的模糊表达提供客观参考具有积极意义,然而,法律信息语义模糊、法规冲突、司法解释具有开放性、司法裁判需要常识,各种隐性知识、过程知识、模糊知识等难以用计算机符号体系表达.认知智能重在提高智能系统对数据理解、知识表达、逻辑推理和自我学习能力,将更好地理解法学规则和分析法律行为.认知法学运用认知智能将提高智能系统对法学问题的理解和认知能力,增强智能裁判的可解释性,从计量法学、计算法学发展到认知法学是法学研究的必然趋势.作为认知社会科学的一部分,认知法学将完善和发展传统法学理论,促进英美法系和大陆法系的深度融合,并有望成为法学研究的新交叉学科分支.

论神经科技时代的神经权及其保障

本文对神经权采取文献研究、总体性评述的研究方法,认为神经科学高速发展,神经科技滥用的风险,还有现行人权结构无法解决涉神经相关问题,这都是神经权提出的原因所在。神经权本身可分为精神隐私权和精神自由、自主、自决权两方面。对于神经权的保障措施方面,可从以下几方面着手:将神经权纳入国际人权保障框架、以神经权为基构建出神经权法治制度体系、神经权权利主体应增强神经权意识。最后,稍作乐观展望,认为神经权也可能推动现有的人权制度走向新的范式。

神经科学偏见效应：可重复性及其心理机制的探索

神经科学的发展对其他学科以及社会产生了重要的影响。虽然神经科学方法与行为研究方法都是探索人类心理与行为的有效手段并各有所长,但神经科学的研究结果却可能引起人们过度的解读和信任。研究者发现,当某一结论使用神经科学结果作为证据时,比使用行为科学结果或者心理生理学指标作为证据时更加让人信服,即使神经科学结果与该结论之间毫无关系,这种现象被称为神经科学偏见(neuroscience bias)。通过系统回顾近年来关于神经科学偏见的研究,我们发现:(1)虽然神经科学偏见存在可重复性的争论,但该效应确实存在;(2)神经科学偏见的产生可能是因为个体倾向于还原论的解释(即使用低层次、简单的机制来解释更高层次上的现象)及心理本质主义的影响(即人们认为心理与行为的本质是神经活动)。神经科学偏见反映了公众对科学结果解读的偏见,未来研究需要探讨这种偏见的心理机制,从而引导科学结果的正确解读和运用。

STUDY ON THE METEOROLOGICAL PREDICTION MODEL USING THE LEARNING ALGORITHM OF NEURAL ENSEMBLE BASED ON PSO ALGORITHMS

Because of the difficulty in deciding on the structure of BP neural network in operational meteorological application and the tendency for the network to transform to an issue of local solution, a hybrid Particle Swarm Optimization Algorithm based on Artificial Neural Network (PSO-BP) model is proposed for monthly mean rainfall of the whole area of Guangxi. It combines Particle Swarm Optimization (PSO) with BP, that is, the number of hidden nodes and connection weights are optimized by the implementation of PSO operation. The method produces a better network architecture and initial connection weights, trains the traditional backward propagation again by training samples. The ensemble strategy is carried out for the linear programming to calculate the best weights based on the "east sum of the error absolute value" as the optimal rule. The weighted coefficient of each ensemble individual is obtained. The results show that the method can effectively improve learning and generalization ability of the neural network.

Chemotherapy of cerebral gliomas directed by the chemosensitivity test in vitro: a clinical study

Objective： To evaluate correlation between chemosensitivity of tumor cells in vitro and their clinical responsiveness in vivo by comparing the difference of curative effect between chemotherapy of cerebral gliomas directed by chemosensitivity test in vitro and its routine chemotherapy. Methods： Sixty-two patients with cerebral gliomas were recruited as the experiment group, who had received total resection or subtotal resection of the tumor. The resected tumor cells were cultured in vitro, followed by chemosensitivity test using colorimetric MTT assay. Finally, chemotherapeutic protocol was made based on the results of the chemosensitivity test. Fifty patients with cerebral gliomas subjected to the routine chemotherapeutic protocol were simultaneously recruited as the control group, whose age, gender, survival functional status and operational fashion were matched with the experiment group. The two groups were equally followed up for the survival functional status, recurrence and death. All data were analyzed using SPSS 10.0 software. Results： At the time of evaluation, KPS values of 64.52 ± 35.84 were seen in the experiment group, and 33.60 ± 36.24 in the control group, showing statistical difference between the two groups （t = 4.5163, P = 0.000）. During 2-4 years of follow up, a recurrence rate of 32.26% was seen in the experimental group, and 60.00% in the control group, showing a statistical difference between the two groups （X^2 = 8.620, P = 0.003）. The fatality was 22.58% in the experiment group, and 48.00% in the control group, showing a statistical difference between the two groups （X^2 = 7.978, P = 0.005）. The survival rate of the experimental group was higher than that of the control group, showing a statistical differences between the two groups （X^2= 7.29, P = 0.0069）. Conclusion： Chemotherapy of glio- mas under the guidance of chemosensitivity test in vitro contributes to obvious improvement on the current survival functional status, a clear decline of the recurrence rates and fatality rate, and raised survival rates of the patients. A close correlation between the chemosensitivity in vitro and clinical responsiveness in vivo is observed.

Ship motion extreme short time prediction of ship pitch based on diagonal recurrent neural network

A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The principle of RPE learning algorithm is to adjust weights along the direction of Gauss-Newton. Meanwhile, it is unnecessary to calculate the second local derivative and the inverse matrixes, whose unbiasedness is proved. With application to the extremely short time prediction of large ship pitch, satisfactory results are obtained. Prediction effect of this algorithm is compared with that of auto-regression and periodical diagram method, and comparison results show that the proposed algorithm is feasible.

Prediction for asphalt pavement water film thickness based on artificial neural network

In order to study the variation o f the asphalt pavement water film thickness influenced by multi-factors,anew method for predicting water film thickness was developed by the combination o f the artificial neural network(ANN)a d two-dimensional shallow water equations based on hydrodynamic theory.Multi-factors included the rainfall intensity,pavement width,cross slope,longitudinal slope a d pavement roughness coefficient.The two-dimensional hydrodynamic method was validated by a natural rainfall event.Based on the design scheme o f Shen-Sha expressway engineering project,the limited training data obtained by the two-dimensional hydrodynamic simulation model was used to predict water film thickness.Furthermore,the distribution of the water film thickness influenced by multi-factors on the pavement was analyzed.The accuracy o f the ANN model was verified by the18sets o f data with a precision o f0.991.The simulation results indicate that the water film thickness increases from the median strip to the edge o f the pavement.The water film thickness variation is obviously influenced by rainfall intensity.Under the condition that the pavement width is20m and t e rainfall intensity is3m m/h,t e water film thickness is below10mm in the fast lane and20mm in t e lateral lane.Athough there is fluctuation due to the amount oftraining data,compared with the calculation on the basis o f the existing criterion and theory,t e ANN model exhibits a better performance for depicting the macroscopic distribution of the asphalt pavement water film.

Research on High Resolution Satellite Image Classification Algorithm based on Convolution Neural Network

Artifi cial neural network is a kind of artificial intelligence method to simulate the function of human brain, and deep learning technology can establish a depth network model with hierarchical structure on the basis of artificial neural network. Deep learning brings new development direction to artificial neural network. Convolution neural network is a new artificial neural network method, which combines artificial neural network and deep learning technology, and this new neural network is widely used in many fields of computer vision. Modern image recognition algorithm requires classifi cation system to adapt to different types of tasks, and deep network and convolution neural network is a hot research topic in neural networks. According to the characteristics of satellite digital image, we use the convolution neural network to classify the image, which combines texture features with spectral features. The experimental results show that the convolution neural network algorithm can effectively classify the image.

LEARNING ALGORITHM OF STAGE CONTROL NBP NETWORK

This letter analyzes the reasons why the known Neural Back Promulgation (NBP)network learning algorithm has slower speed and greater sample error. Based on the analysis and experiment, the training group descending Enhanced Combination Algorithm (ECA) is proposed.The analysis of the generalized property and sample error shows that the ECA can heighten the study speed and reduce individual error.

An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process

The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark process is studied using Neur-On-Line, a graphical tool kit for developing and deploying neural networks in the G2 real time intelligent environment,and a new modified Broyden, Fletcher, Goldfarb, and Shanno （BFGS） quasi-Newton algorithm. The modified BFGS algorithm for the adaptive learning of back propagation （BP） neural networks is developed and embedded into NeurOn-Line by introducing a new search method of learning rate to the full memory BFGS algorithm. Simulation results show that the adaptive learning and prediction neural network system can quicklv track the time-varving and nonlinear behavior of the bioreactor.

Neurotrophic and metabotrophic potential of nerve growth factor and brain-derived neurotrophic factor: Linking cardiometabolic and neuropsychiatric diseases

One of biggest recent achievements of neurobiology is the study on neurotrophic factors. The neurotrophins are exciting examples of these factors. They belong to a family of proteins consisting of nerve growth fac-tor （NGF）, brain-derived neurotrophic factor （BDNF）, neurotrophin-3 （NT-3）, NT-4/5, NT-6, and NT-7. Today, NGF and BDNF are well recognized to mediate a diz-zying number of trophobiological effects, ranging from neurotrophic through immunotrophic and epitheliotro-phic to metabotrophic effects. These are implicated in the pathogenesis of various diseases. In the same vein, recent studies in adipobiology reveal that this tissue is the body’s largest endocrine and paracrine organ producing multiple signaling proteins collectively termed adipokines, with NGF and BDNF being also produced from adipose tissue. Altogether, neurobio-logy and adipobiology contribute to the improvement of our knowledge on diseases beyond obesity such as cardiometabolic （atherosclerosis, type 2 diabetes, and metabolic syndrome） and neuropsychiatric （e.g. , Alzheimer’s disease and depression） diseases. The present review updates evidence for （1） neurotrophic and metabotrophic potentials of NGF and BDNF linking the pathogenesis of these diseases, and （2） NGF- and BDNF-mediated effects in ampakines, NMDA receptor antagonists, antidepressants, selective deacetylase inhibitors, statins, peroxisome proliferator-activated receptor gamma agonists, and purinergic P2X3 recep-tor up-regulation. This may help to construct a novel paradigm in the feld of translational pharmacology of neuro-metabotrophins, particularly NGF and BDNF.

High-Performance of Power System Based upon ANFIS （Adaptive Neuro-Fuzzy Inference System） Controller

The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorithm. A tuning method is proposed for training of the neuro-fuzzy controller. The best rule base and the best training algorithm chosen produced high performance in the ANFIS controller. Simulation was done on Matlab Ver. 2010a. A case study was chopper-fed DC motor drive, in continuous and discrete modes. Satisfactory results show the ANFIS controller is able to control dynamic highly-nonlinear systems. Tuning it further improved the results.

Prediction of 2A70 aluminum alloy flow stress based on BP artificial neural network

The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over 360～480 ℃ with strain rates in the range of 0.01～1 s-1 and the largest deformation up to 60%. On the basis of experiments, a BP artificial neural network （ANN） model was constructed to predict 2A70 aluminum alloy flow stress. True strain, strain rates and temperatures were input to the network, and flow stress was the only output. The comparison between predicted values and experimental data showed that the relative error for the trained model was less than ±3% for the sampled data while it was less than ±6% for the non-sampled data. Furthermore, the neural network model gives better results than nonlinear regression method. It is evident that the model constructed by BP ANN can be used to accurately predict the 2A70 alloy flow stress.

Association between HER-2 Over-Expression and Prognosis in Human Osteosarcoma:a Meta-Analysis

OBJECTIVE Various studies examining the relationship be-tween HER-2 over-expression and the response to chemotherapy and clinical outcome in patients with osteosarcoma have yielded inconclusive results.The purpose of the current study was to evaluate the relation of HER-2 status with the response to chemo-therapy and clinical outcome in osteosarcoma.METHODS We conducted a meta-analysis of 6 studies that evaluated the correlation between HER-2 status and histologic response to chemotherapy and 2-year survival.Data were syn-thesized in summary receiver operating characteristic curves and with summary likelihood ratios(LRs) and relative risk.RESULTS The quantitative synthesis showed that HER-2 status is not a prognostic factor for the response to chemotherapy.The positive LR was 1.27(95% conf idence interval,0.91～1.77),and the negative LR was 0.68(95% confidence interval,0.38～1.22).There was no significant between-study heterogeneity.HER2-positive status tended to be associated with a worse 2-year survival,but the overall results were not formally statistically signif icant.CONCLUSION HER-2 status is not associated with the histo-logic response to chemotherapy in patients with osteosarcoma,whereas HER-2 positive patients may be associated with decreased survival.

Identification and novel adaptive fuzzy control of nonlinear system for PEMFC stack

The operating temperature of a proton exchange membrane fuel cell stack is a very important control parameter. It should be controlled within a specific range, however, most of existing PEMFC mathematical models are too complicated to be effectively applied to on-line control. In this paper, input-output data and operating experiences will be used to establish PEMFC stack model and operating temperature control system. An adaptive learning algorithm and a nearest-neighbor clustering algorithm are applied to regulate the parameters and fuzzy rules so that the model and the control system are able to obtain higher accuracy. In the end, the simulation and the experimental results are presented and compared with traditional PID and fuzzy control algorithms.