The purpose of this paper is twofold. Firstly, to investigate the merit of estimating probability density functions rather than level or classification estimations on a one-day-ahead forecasting the task of the silver time series.
This is done by benchmarking the Gaussian mixture neural network model (as a probability distribution predictor) against two other neural network designs representing a level estimator (the Mulit-layer perceptron network [MLP]) and a classification model (Softmax cross entropy network model [SCE]). In addition, we also benchmark the results against standard forecasting models, namely a naive model, an autoregressive moving average model (ARMA) and a logistic regression model (LOGIT).
The second purpose of this paper is to examine the possibilities of improving the trading performance of those models by applying confirmation filters and leverage.
As it turns out, the three neural network models perform equally well generating a recognisable gain while ARMA benchmark model, on the other hand, seems to have picked up the right rhythm of mean reversion in the silver time series, leading to very good results. Only when using more sophisticated trading strategies and leverage, the neural network models show an ability to identify successfully trades with a high Sharpe ratio and outperform the ARMA model.
Studies were conducted to investigate the distribution of larvae of the European vine moth, Lobesia botrana (Denis & Schiffermüller) (Lepidoptera: Tortricidae), a key vineyard pest of grape cultivars. The data collected were larval densities of the second and third generation of L. botrana on half-vine and entire plants of wine and table cultivars in 2003-2004. No insecticide treatments were applied to plants during the 2-year study. The distribution of L. botrana larvae can be described by a negative binomial. This reveals that the insect aggregates. A common value for the k parameter of the negative binomial distribution of kc = 0.6042, was obtained, using maximum likelihood estimation, and the advantages and cases of use of a common k are discussed. The k-1Sinh-1(ksqrt{x+1/2}) and k-1Sinh-1(ksqrt{x+3/8}) proved to be the best transformations for L. botrana larval counts. An entire vine is recommended as the sampling unit for research purposes, whereas a half-vine, which is suitable for grape vine cultivation in northern Greece, is recommended for practical purposes. We used these findings to develop a fixed precision sequential sampling plan and a sequential sampling program for classifying the pest status of L. botrana larvae.
This paper addresses the problem of probability estimation in Multiclass classification tasks combining two well-known data mining techniques: Support Vector Machines and Neural Networks. We present an algorithm which uses both techniques in a two-step procedure. The first step employs Support Vector Machines within a One-vs-All reduction from multiclass to binary approach to obtain the distances between each observation and the Support Vectors representing the classes. The second step uses these distances as inputs for a Neural Network, built with an entropy cost function and softmax transfer function for the output layer where class membership is used for training. Consequently, this network estimates probabilities of class membership for new observations. A benchmark using different databases demonstrates that the proposed algorithm is highly competitive with the most recent techniques for multiclass probability estimation.
In the developing brain, mature brain derived neurotrophic factor (mBDNF) and its precursor (proBDNF) exhibit prosurvival and proapoptotic functions, respectively. However, it is still unknown whether mBDNF or proBDNF is a major form of neurotrophin expressed in the immature brain, as well as if the level of active caspase -3 correlates with the levels of BDNF forms during normal brain development. Here we found that both proBDNF and mBDNF were expressed abundantly in the rat brainstem, hippocampus and cerebellum between embryonic day 20 and postnatal day 8. The levels of mature neurotrophin as well as mBDNF to proBDNF ratios negatively correlated with the expression of active caspase -3 across brain regions. The immature cortex was the only structure, in which proBDNF was the major product of bdnf gene, especially in the cortical layers 2-3. And only in the cortex, the expression of BDNF precursor positive ly correlated with the levels of active caspase -3. These findings suggest that proBDNF alone may play an important role in the regulation of naturally occurring cell death during cortical development., P. N. Menshanov, D. A. Lanshakov, N. N. Dygalo., and Obsahuje bibliografii
The scientific and political communities must be aware of our bias in the knowledge of the taxonomy of the various living organisms. Although the effects of species concepts on conservation have received considerable attention, usage of the subspecies category in conservation lists have received insufficient scientific scrutiny, at least for most taxonomic groups and geographic regions. Here we draw from the class Mammalia to show that discrepancies in the inclusion of subspecies in the IUCN Red List often reflect uneven taxonomic knowledge and the differential scientific and public interest raised by different kinds of mammals, which together can produce a biased picture of mammalian endangerment worldwide.
Cíl: Korektní odvození přípustných limitů preciznosti pro správné nastavení regulačních diagramů a efektivní řízení interní kontroly kvality (IKK) v laboratorní praxi. Metodika: Aplikace 3-signální tabulky dle návrhu Haeckela a Wosnioka [1] pro výpočet přípustných limitů preciznosti z empirické biologické variability (CV E ) založené na referenčních intervalech a jejich následné použití pCV A v regulačních diagramech. Diskuse: Možnosti použití 3-signální tabulky [1]. Závěr: Varování před nesprávným použitím regulačních diagramů; správné odvození přípustných limitů mezilehlé preciznosti jako klíč pro efektivní použití regulačních diagramů a pro objektivní třídění jednotlivých laboratorních metod podle jejich kvality., Objective: Presentation an advanced method for deriving correct permissible precision limits which are required for the correct use of control charts, and for the effective internal quality control management (IQC) in a laboratory practice. Method: The 3-signal table application designed by Haeckel and Wosniok [1], which provides appropriate means to calcu - late the correct permissible precision limits (pCV A ) from the empirical biological variability (CV E ), based on reference intervals (RIs); the subsequent proper use of pCV A in the control charts. Discussion: Potential uses of the 3-signal table [1]. Conclusion: Warning against an improper use of the control charts; the correct method for deriving the permissible inter - mediate precision limits as the key ingredient for setting up effective control charts, which are a prerequisite for an objective classification of different laboratory methods according to their real quality, Ambrožová J., and Literatura