A Turing test of a timing theory

A quantitative theory of timing or conditioning can be evaluated with a Turing test in which the behavioral results of an experiment can be compared with the predicted results from the theory. An example is described based upon an experiment in which 12 rats were trained on three fixed-interval schedules of reinforcement, and a simulation of the predicted results from a packet theory of timing. An objective classification rule was used to determine whether a sample from the data or a sample from the theory was more similar to another sample from the theory. With an ideal theory, the expected probability of a correct classification would be 0.5. The observed probability of a correct classification was 0.6, which was slightly, but reliably, greater than 0.5. A Turing test provides a graded metric for the evaluation of a quantitative theory.     

模糊图论分类的截取水平选择

在数量分类中,模糊图论的最大树方法可直接依据模糊相似系数得到树状图。但至目前为止,模糊图论分类中的截取水平通常由分析者主观给定,或者是以逐步试验、逐步修改的方法确定的。用太白山针叶林的数量分类研究结果表明,模糊图论分类的截取水平应选在模糊关系变化较大处,并可用数学方法确定。研究的植被分类实例结果与模糊聚类分析是一致的。     

Process Simulation and Cost Analysis for Removing Inorganics from Wood Chips Using Combined Mechanical and Chemical Preprocessing

Inorganic species (ash) in biomass feedstocks negatively impact thermochemical and biochemical energy conversion processes. In this work, a process simulation model is developed to model the reduction in ash content of loblolly logging residues using a combination of air classification and dilute-acid leaching. Various scenarios are considered, and it is found that costs associated with discarding high-ash material from air classification are substantial. The costs of material loss can be reduced by chemical leaching the high-ash fraction obtained from air classification. The optimal leaching condition is found to be approximately 0.1 wt% sulfuric acid at 24 °C. In example scenarios, total process costs in the range of $6–9?/dry tons of product are projected that result in a removal of 14, 62, 39, and 88 % of organics, total ash (inorganics), alkaline earth metals and phosphorus (AAEMS + P), and silicon, respectively. Sensitivity analyses indicate that costs associated with loss of organic material during processing (yield losses), brine disposal, and labor have the greatest potential to impact the total processing cost.     

State Classification for Human Hands

The human hand, which is a perfect model for dextrous hands, is a masterpiece of mechanical complexity. A full understanding of the human hand will provide more inspiration for the successful implementation and application of dextrous hands. The purpose of this paper is two-fold. First, the characteristic tree of the human hand is established for kinematic characteristics analysis. Second, a systematic classification for the human hand states is issued from the view point of topology. Moreover, the kinematic characteristics of the palm or finger tips are achieved via the Generalized Function (G_F) set theory with the aim of achieving deeper insight into the capabilities of the human hand. Finally one application example is given to show the effectiveness of the exploitation of the G_F set theory and the usefulness of this methodology for dextrous hands.     

A theory of evaluation applied to human-environment systems

1. 1. The writers present the general theory of evaluation that is being developed by their group.

2. 2. The evaluation of a human environment is a complex mental process.

3. 3. In an effort to express numerically the quality of an environment, one tends to oversimplify the complex aspects of it and the entailing problems in relation to its inhabitants.

4. 4. In this paper, some examples are taken in the evaluation of thermal environments, wherein much has been said and done in setting up numerical scales to express human comfort, and yet neither clear-cut explanations nor convincing logic seem to exist to terminate the argument over the widely scattered and sometimes seemingly contradicting experimental data.

5. 5. The writers suggest that many of the reasons for this confusion may be traced back to the oversimplified notion of evaluation.

6. 6. It is shown that there are various possibilities when looking at the scales of evaluation.

7. 7.|The nominal scale, least studied of all the four traditional scales, may be given a prominent place in evaluating a thermal environment. The pseudo-interval order scale is another example.

太白山针叶林的Fuzzy分类研究

应用模糊聚类分析法和模糊图论分析对太白山针叶林进行了数量分类比较研究。将２６个样地分为两大类共７个群落类型。研究结果表明,两种方法在植物群落分类研究中,不但是可行的,而且所分类的实际结果是等价的,与实际观测情况也是吻合的。其中的图论法直接依据模糊相似系数得到树状图,简便易行,显示出更大的适用性。     

A simple method for classifying genes and a bootstrap test for classifications

A new simple method for classifying genes is proposed based on Klastorin's method. This method classifies genes into monophyletic groups which are made distinct from each other by evolutionary changes. The method is applicable as long as the phylogenetic tree of genes is obtained. There is a fast algorithm for obtaining the classification. A bootstrap test of a classification is also presented. As an example, we classified opsin genes. The classification obtained by this method is the same as the previous classification based on the function of opsins.     

Application of Boosting Classification and Regression to Modeling the Relationships Between Trace Elements and Diseases

The study on the relationship between trace elements and diseases often need to build a classification/regression model. Furthermore, the accuracy of such a model is of particular importance and directly decides its applicability. The goal of this study is to explore the feasibility of applying boosting, i.e., a new strategy from machine learning, to model the relationship between trace elements and diseases. Two examples are employed to illustrate the technique in the applications of classification and regression, respectively. The first example involves the diagnosis of anorexia according to the concentrations of six elements (i.e. classification task). Decision stump and support vector machine are used as the weak/base algorithm and reference algorithm, respectively. The second example involves the prediction of breast cancer mortality based on the intake of trace elements (i.e. a regression task). In this regard, partial least squares is not only used as the weak/base algorithm, but also the reference algorithm. The results from both examples confirm the potential of boosting in modeling the relationship between trace elements and diseases.     

An associative memory that can form hypotheses: a phase-coded neural network

 Nonlinear associative memories as realized, e.g., by Hopfield nets are characterized by attractor-type dynamics. When fed with a starting pattern, they converge to exactly one of the stored patterns which is supposed to be most similar. These systems cannot render hypotheses of classification, i.e., render several possible answers to a given classification problem. Inspired by von der Malsburg’s correlation theory of brain function, we extend conventional neural network architectures by introducing additional dynamical variables. Assuming an oscillatory time structure of neural firing, i.e., the existence of neural clocks, we assign a so-called phase to each formal neuron. The phases explicitly describe detailed correlations of neural activities neglected in conventional neural network architectures. Implementing this extension into a simple self-organizing network based on a feature map, we present an associative memory that actually is capable of forming hypotheses of classification. Received: 6 December 1993/Accepted in revised form: 14 July 1994     

A Reexamination of the Arachnid Theory on the Origin of Vertebrata

A method of phylogenetic classification, based on Hennig's theory of cladism and Sokal and Sneath's theory of numerical taxonomy, is outlined. The necessity of using non-morphological characters in superphyletic classification is argued. It is suggested that the vertebrates arose from members of the annelid superphylum, an alternative implied by the Arachnid theory advocated by Gaskell and Patten. A testing of this theory by reference to non-morphological characters corroborates the affiliation between Vertebrata and the annelid superphyium, but the available data do not warrant a definite answer as concerns the theory.     

中国地方品种鸡的分类研究

中国鸡种资源极为丰富,中国历史上形成的许多优良地方品种鸡,对中国养鸡事业发展和世界养鸡业都曾做出过重要贡献。为充分利用我国地方鸡种资源遗传潜力和杂种优势,本文利用图论主成分分类法与系统聚类法根据20项指标研究了中国30个地方品种鸡的分类问题,分类结果比较符合实际,特别是图论主成分分类图直观清楚,生物学含意明确,对中国鸡种资源和基因库的利用有实际参考价值。     

Observations on the Classification and Distribution of Pulmonary Emphysema in Canada

General principles of epidemiology are reviewed, using as an example the chronic non-specific respiratory diseases in Canada. Lack of agreement in definition and classification seriously hampers the use of governmental statistics to study the epidemiology of these diseases. Though Canada is experiencing a mid-century epidemic of deaths from emphysema, evidence is presented which suggests that provincial variation in this death rate is artificial and is likely apparent because physicians do not categorize deaths from this and related diseases in any similar fashion. Three-year-average age-standardized death rates by provincial areas for emphysema with or without bronchitis, asthma, chronic bronchitis and bronchiectasis are presented in evidence that provincial variation is largely obliterated when all chronic non-specific respiratory diseases are considered as a single nosological unit. Advantages and disadvantages of a recent clinical classification of these diseases are reviewed, and it is concluded that such a classification has a great deal to offer epidemiologists or clinicians studying the cause and natural history of these diseases.     

Classification and Visualization Based on Derived Image Features: Application to Genetic Syndromes

Data transformations prior to analysis may be beneficial in classification tasks. In this article we investigate a set of such transformations on 2D graph-data derived from facial images and their effect on classification accuracy in a high-dimensional setting. These transformations are low-variance in the sense that each involves only a fixed small number of input features. We show that classification accuracy can be improved when penalized regression techniques are employed, as compared to a principal component analysis (PCA) pre-processing step. In our data example classification accuracy improves from 47% to 62% when switching from PCA to penalized regression. A second goal is to visualize the resulting classifiers. We develop importance plots highlighting the influence of coordinates in the original 2D space. Features used for classification are mapped to coordinates in the original images and combined into an importance measure for each pixel. These plots assist in assessing plausibility of classifiers, interpretation of classifiers, and determination of the relative importance of different features.     

A machine-learning method for classifying and analyzing foot placement: Application to manual material handling

Foot placement strategy is an essential aspect in the study of movement involving full body displacement. To get beyond a qualitative analysis, this paper provides a foot placement classification and analysis method that can be used in sports, rehabilitation or ergonomics. The method is based on machine learning using a weighted k-nearest neighbors algorithm. The learning phase is performed by an observer who classifies a set of trials. The algorithm then automatically reproduces this classification on subsequent sets. The method also provides detailed analysis of foot placement strategy, such as estimating the average foot placements for each class or visualizing the variability of strategies. An example of applying the method to a manual material handling task demonstrates its usefulness. During the lifting phase, the foot placements were classified into four groups: front, contralateral foot behind, ipsilateral foot behind, and parallel. The accuracy of the classification, assessed with a holdout method, is about 97%. In this example, the classification method makes it possible to observe and analyze the handler’s foot placement strategies with regards to the performed task.     

Quantitative classification of life-style types in predaceous phytoseiid mites

Classification of species into different functional groups based on biological criteria has been a difficult problem in ecology. The difficulty mainly arises because natural classification patterns are not necessarily mutually exclusive. The more group characteristics overlap, the more difficult it is to identify the membership of a species in the overlapping portions of any two groups. In this paper, we present an application of discriminant analysis by creating classification models from life history and morphological data for two specialist and two generalist life-styles type of predaceous phytoseiid mites. Two stages can be distinguished in our method: life-style group membership assignment and trait variable evaluation. We use a Bayesian framework to create a classifier system to locate or assign species within a mixture of trait distributions. The method assumes that a mixture of trait distributions can represent the multiple dimensions of biological data. The mixture is most evident near the boundaries between groups. Because of the complexity of analytical solution, an iterative method is used to estimate the unknown means, variances, and mixing proportion between groups. We also developed a criterion based on information theory to evaluate model performance with different combinations of input variables and different hypotheses. We present a working example of our proposed methods. We apply these methods to the problem of selecting key species for inoculative release and for classical introductions of biological pest control agents.     

模糊ＩＳＯＤＡＴＡ在草地植物群落分类上的应用

 模糊集合理论在元素与集合的关系中引入了连续变化的隶属度,因而能适应植物群落作连续和明显间断分布同时存在的情况。本文运用以模糊集合理论为基础的ISODATA聚类分析方法对松嫩平原草地植物群落进行了数量分类,结果说明模糊ISODATA聚类比传统的分类方法有更为客观、灵活的特点,是一种较为理想的植被数量分类方法。     

Pareto-based multi-objective optimization for classification in data mining

This paper explores the possibility of classification based on Pareto multi-objective optimization. The efforts on solving optimization problems using the Pareto-based MOO methodology have gained increasing impetus on comparison of selected constraints. Moreover we have different types of classification problem based on optimization model like single objective optimization, MOO, Pareto optimization and convex optimization. All above techniques fail to generate distinguished class/subclass from existing class based on sensitive data. However, in this regard Pareto-based MOO approach is more powerful and effective in addressing various data mining tasks such as clustering, feature selection, classification, and knowledge extraction. The primary contribution of this paper is to solve such noble classification problem. Our work provides an overview of the existing research on MOO and contribution of Pareto based MOO focusing on classification. Particularly, the entire work deals with association of sub-features for noble classification. Moreover potentially interesting sub-features in MOO for classification are used to strengthen the concept of Pareto based MOO. Experiment has been carried out to validate the theory with different real world data sets which are more sensitive in nature. Finally, experimental results provide effectiveness of the proposed method using sensitive data.     

Low Values of the Scheraga–Mandelkern β parameter for proteins. An explanation based on porous sphere hydrodynamics

Several globular proteins have values of the Scheraga–Mandelkern β parameter significantly below the theoretical minimum value, β₀ = 2.112 × 10⁶, for an impermeable sphere. Using the Felderhof–Deutch generalization of the Debye–Bueche–Brinkman theory of hydrodynamics of porous spheres, we have shown that values of β slightly below this supposed minimum are theoretically expected. A porous sphere of uniform density has a minimum β of 2.084 × 10⁶ at a Debye shielding ratio of 6.5, corresponding, for example, to a sphere radius of 11 Å and an inverse hydrodynamic shielding length of 0.6 Å^?1, values not far from those of small proteins. A two-layer porous sphere model gives similar results. Although this is the first theoretical explanation of values of β below β₀, the theory is incomplete since β values as low as 2.03 × 10⁶ are observed.     

A hierarchical classification of first-order recurrent neural networks

We provide a decidable hierarchical classification of first-order recurrent neural networks made up of McCulloch and Pitts cells. This classification is achieved by proving an equivalence result between such neural networks and deterministic Büuchi automata, and then translating the Wadge classification theory from the abstract machine to the neural network context. The obtained hierarchy of neural networks is proved to have width 2 and height omega + 1, and a decidability procedure of this hierarchy is provided. Notably, this classification is shown to be intimately related to the attractive properties of the considered networks.