The addition of the infinitive to the subject or Object is one of the syntactic structures in which accuracy plays an important role in the correct understanding the meaning of the verses of the Holy Quran. Sometimes there is disagreement among the scholars who interpret the Holy Book of Quran in determining whether the infinitive is added to the subject or to the Object. Translators have sometimes provided an inadequate translation. This article has tried to discover ways to resolve the difference in determining the qualificative role of genitive case and also to provide an expressive translation of this structure by using a descriptive-analytical-critical method and by examining a number of infinitives added to the subject or Object in the Holy Quran. The solutions to resolve the dispute are as follows: Paying attention to how the sentences relate to each other, paying attention to the citation of the subject in the verse, paying attention to the reason for using rhetorical devices in the verse, paying attention to the interpretive context (the context of the discussed verse, the guiding purpose of the Surah), paying attention to the verses and narrations of the same subject of the verse and paying attention to the requirements of servant politeness. The use of these solutions in order to provide a correct and expressive translation of the infinitive structure added to the subject or the Object is necessary: The use of verb in the translation of the infinitive, the use of an appropriate word for translation of the pronoun, the use of an appropriate word in addition to the accompanying added infinitive, the use of the explanations in parentheses, the avoidance of compression writing and the avoidance of literal translation.

Extracting bottlenecks improves considerably the speed of learning and the ability knowledge transferring in reinforcement learning. But, extracting bottlenecks is a challenge in reinforcement learning and it typically requires prior knowledge and designer’s help. This paper will propose a new method that extracts bottlenecks for reinforcement learning agent automatically. We have inspired of biological systems, behavioral analysts and routing animals and the agent works on the basis of its interacting to environment. The agent finds landmarks based in clustering and hierarchical Object Recognition. If these landmarks in actions space are close to each other, bottlenecks are extracted using the states between them. The Experimental results show a considerable improvement in the process of learning in comparison to some key methods in the literature.

Introduction: Faces can be speedily processed, although they convey an immense amount of information. Hence, in psychophysiological experiments, human faces constitute very special stimuli. Numerous studies have investigated the electrophysiological correlates of face processing, showing the existence of multiple event-related components. Nevertheless, dissimilarities in various levels of processing are still controversial. In this study, we used magnetoencephalography (MEG) to examine how facial processing is different in perception and Recognition from Object processing.  Methods: In this event‐related potential study, the differences between face and Object processing stages were assessed. The participants were 22 healthy individuals. Three types of stimuli, including human face, monkey face, and motorbike, were projected for 200 ms onto a screen placed 90 cm in front of participants’ eyes while they sat under the MEG helmet. The participants viewed images of the same type in sequential order and had to decide the equality of the second image compared to the first image in a response window of 1 second. This procedure was repeated 48 times per stimulus. Additionally, we compared the perception and Recognition per stimulus type. The neuromagnetic responses were recorded with the VectorViewTM MEG system. We used the FieldTrip toolbox for EEG/MEG-analysis. Results: Our results confirmed the face-selectivity for the M170 component, but not always for the M100 component. We also observed a unique speed pattern for the M170 component in perception and Recognition at the onset and the peak time. Conclusion: Our findings showed an early face-selective component in Recognition but not always in perception. Considering the onset and the peak time, there is no difference between various comparisons in this early component.

Human visual system can recognize Object accurately, swiftly, and effortlessly even when Objects are under challenging conditions. Many research groups try to model this ability; however, these computational models could not achieve human performance. Convolutional neural networks (CNN’ s) are the state-of-the-art successful computational vision models that try to implement feedforward path of human visual system. However, evidence shows that human visual system uses top-down expectation signals to increase accuracy and speed of Object Recognition under dificult conditons. In this study, we extend a well-known model using top-down expectation signals. In this regard, Alexnet network is considered as feedforward path. We used a pre-trained network on ImageNet dataset for Object Recognition and a pre-trained network on Places dataset for scene Recognition. The pre-trained network on places was used to provide top-down feedback signals based on scene information. The feedback signals contain occurrence frequency information of the Objects in the scene. These signals are integrated with information from feedforward path. To evaluate the proposed model several experiments were done on different image sets. The results showed that integrating the feedback information with the feedforward information significantly improve Object Recognition accuracy in comparison to the base model. This support the idea that content information facilitates Object Recognition ability, specifically when Objects are under challenging conditions.

The proposed method is to recognize Objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Then, Local Steering Kernel is applied to the resultant pixels in order to obtain the most promising features. The features extracted will be over complete, so, in order to reduce dimensionality, Principal Component Analysis (PCA) is applied. Further, the sparse histogram is taken over the PCA output. The classifier used here is Support Vector Machine (SVM) Classifier. Bench mark database used is UIUC car database and the results obtained are satisfactory. The results obtained using LSK kernel is compared by varying parameters such as patch size, number of salient points/patches, smoothing parameter and scaling parameter.