Text sentiment analysis plays an important role in social network information mining. It is also the theoretical foundation and basis of personalized recommendation, circle of interest classification and public opinion analysis. In view of the existing algorithms for feature extraction and weight calculation, we find that they fail to fully take into account the in fluence of sentiment words. Therefore, this paper proposed a fine-grained short text sentiment analysis method based on machine learning. To improve the calculation method of feature selection and weighting and proposed a more suitable sentiment analysis algorithm for features extraction named N-CHI and weight calculation named W-TF-IDF, increasing the proportion and weight of sentiment words in the feature words Through experimental analysis and comparison, the classification accuracy of this method is obviously improved compared with other methods.
This article reports a method for forecasting an earthquake by synchronous anomalies of optical astronomic time-latitude residuals. The so-called optical astronomic time-latitude residuals for a certain astrometric instrument are the rest after deducting the effects of Earth whole motion from the astronomical time and latitude observations determined by the instrument. Forecasting practice for four earthquakes around the Yunnan Observatory occurring after 2010 shows that it does not generate false forecasts, and also does not miss forecasts of major earthquakes. This forecasting practice proves that the synchronous anomalies of astronomical time-latitude residuals can provide effective warning sign for earthquake occurrence around observatory station, thus deserves attention and further study. and Su Youjin, Gao Yuping, Hu Hui.
Phthalates are chemicals interfering with the function of testosterone and are suspected to play a role in the emergence of neurodevelopmental diseases. This could be due to interference with brain development for which optimal testosterone levels are essential. We investigated the effect of prenatal and early postnatal exposure to a phthalate mixture on the anogenital distance (AGD), plasma testosterone levels and social behavior in rats. Pregnant rats were exposed to a mixture of diethylhexyl, diisononyl and dibutyl phthalate, each at a dose of 4.5 mg/kg/day, from gestational day 15 to postnatal day 4. A social interaction test was performed to assess sociability in the three ontogenetic stages (weaning, puberty, adulthood). AGD was measured in adulthood to assess changes in prenatal testosterone levels. Plasma testosterone levels were measured in adults by a radioimmunoassay. The total frequency and time of socio-cohesive interactions were decreased in phthalate exposed females in weaning, puberty and adulthood. Phthalate exposed males showed a decrease in the frequency of social interactions in weaning only. Shorter anogenital distance was observed in adult males exposed to phthalates. Decreased testosterone levels were observed in the exposed group in both sexes. Our results suggest that early developmental phthalate exposure may play an important role in the hormonal and behavioral changes associated with several neurodevelopmental diseases.
A mixture of support vector machines (SVMs) is proposed for time series forecasting. The SVMs mixture is composed of a two-stage architecture. In the first stage, self-organizing feature map (SOM) is used as a clustering algorithm to partition the whole input space into several disjointed regions. A tree-structured architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVMs, also called SVM mixture, that best fit partitioned regions are constructed by finding the most appropriate kernel function and the optimal free parameters of SVMs. The experiment shows that the SVMs mixture achieves significant improvement in the generalization performance in comparison with the single SVMs model. In addition, the SVMs mixture also converges faster and use fewer support vectors.
The functional structure of our new network is not preset; instead, it
comes into existence in a random, stochastic manner.
The anatomical structure of our model consists of two input “neurons”, hundreds up to five thousands of hidden-layer “neurons” and one output “neuron”.
The proper process is based on iteration, i.e., mathematical operation governed by a set of rules, in which repetition helps to approximate the desired result.
Each iteration begins with data being introduced into the input layer to be processed in accordance with a particular algorithm in the hidden layer; it then continues with the computation of certain as yet very crude configurations of images regulated by a genetic code, and ends up with the selection of 10% of the most accomplished “offspring”. The next iteration begins with the application of these new, most successful variants of the results, i.é., descendants in the continued process of image perfection. The ever new variants (descendants) of the genetic algorithm are always generated randomly. The determinist rule then only requires the choice of 10% of all the variants available (in our case 20 optimal variants out of 200).
The stochastic model is marked by a number of characteristics, e.g., the initial conditions are determined by different data dispersion variance, the evolution of the network organisation is controlled by genetic rules of a purely stochastic nature; Gaussian distribution noise proved to be the best “organiser”.
Another analogy between artificial networks and neuronal structures lies in the use of time in network algorithms.
For that reason, we gave our networks organisation a kind of temporal development, i.e., rather than being instantaneous; the connection between the artificial elements and neurons consumes certain units of time per one synapse or, better to say, per one contact between the preceding and subsequent neurons.
The latency of neurons, natural and artificial alike, is very importaiit as it
enables feedback action.
Our network becomes organised under the effect of considerable noise. Then, however, the amount of noise must subside. However, if the network evolution gets stuek in the local minimum, the amount of noise has to be inereased again. While this will make the network organisation waver, it will also inerease the likelihood that the erisis in the local minimum will abate and improve substantially the state of the network in its self-organisation.
Our system allows for constant state-of-the-network reading by ineaiis of establishing the network energy level, i.e., basically ascertaining progression of the network’s rate of success in self-organisation. This is the principal parameter for the detection of any jam in the local minimum. It is a piece of input information for the formator algorithm which regulates the level of noise in the system.
We consider a construction of approximate confidence intervals on the variance component σ21 in mixed linear models with two variance components with non-zero degrees of freedom for error. An approximate interval that seems to perform well in such a case, except that it is rather conservative for large σ21/σ2, was considered by Hartung and Knapp in \cite{hk}. The expression for its asymptotic coverage when σ21/σ2→∞ suggests a modification of this interval that preserves some nice properties of the original and that is, in addition, exact when σ21/σ2→∞. It turns out that this modification is an interval suggested by El-Bassiouni in \cite{eb}. We comment on its properties that were not emphasized in the original paper \cite{eb}, but which support use of the procedure. Also a small simulation study is provided.
This paper proposes an offline gradient method with smoothing L1/2 regularization for learning and pruning of the pi-sigma neural networks (PSNNs). The original L1/2 regularization term is not smooth at the origin, since it involves the absolute value function. This causes oscillation in the computation and difficulty in the convergence analysis. In this paper, we propose to use a smooth function to replace and approximate the absolute value function, ending up with a smoothing L1/2 regularization method for PSNN. Numerical simulations show that the smoothing L1/2 regularization method eliminates the oscillation in computation and achieves better learning accuracy. We are also able to prove a convergence theorem for the proposed learning method.
A modified method for clearing, staining and mounting plant-inhabiting mites is proposed. With this method, all mites can be cleared with a single agent and mounted in a single medium. The stain added to the mounting medium enhances the clarity of the fine structures of soft-bodied mites under phase contrast and more significantly under bright field microscopy.
In this paper, Runge-Kutta methods are discussed for numerical solutions of conservative systems. For the energy of conservative systems being as close to the initial energy as possible, a modified version of explicit Runge-Kutta methods is presented. The order of the modified Runge-Kutta method is the same as the standard Runge-Kutta method, but it is superior in energy-preserving to the standard one. Comparing the modified Runge-Kutta method with the standard Runge-Kutta method, numerical experiments are provided to illustrate the effectiveness of the modified Runge-Kutta method.
The phylogenetic relationships of the subgenera Polyommatus and Plebicula, within the Palaearctic butterfly genus Polyommatus, were inferred from a combined analysis of the nuclear marker ITS2 and the barcoding section of the mitochondrial gene COI. Eight major clades were recovered within Polyommatus s. l., which correspond closely to subgenera based on traditional systematics and are of late Pliocene to early Pleistocene origin. Extraordinary chromosomal evolution occurred independently in three of these clades. The disputed position of several species formerly placed in the subgenus Plebicula is clarified. A group of Central Asian species (Bryna) was recovered as a monophyletic clade within Polyommatus s. str. The Kurdistanian endemic P. buzulmavi appears as a sister species to P. icarus. P. celina replaces P. icarus in NW Africa and the Canary Islands, and split from the last common ancestor with P. icarus back in the early Pleistocene.