Priming Neural Networks

Visual priming is known to affect the human visual system to allow detection of scene elements, even those that may have been near unnoticeable before, such as the presence of camouflaged animals. This process has been shown to be an effect of top-down signaling in the visual system triggered by the said cue. In this paper, we propose a mechanism to mimic the process of priming in the context of object detection and segmentation. We view priming as having a modulatory, cue dependent effect on layers of features within a network. Our results show how such a process can be complementary to, and at times more effective than simple post-processing applied to the output of the network, notably so in cases where the object is hard to detect such as in severe noise. Moreover, we find the effects of priming are sometimes stronger when early visual layers are affected. Overall, our experiments confirm that top-down signals can go a long way in improving object detection and segmentation.Comment: fixed error in author nam

Masks of Authenticity: Visual Representation of the Self, Self-stereotyping, and the Question of Visibility in the Age of Neo-Imperialism

This thesis is about Third World artists, the way they are represented in the neo-imperialist core and the way they re-subjectify themselves to fit into its symbolic order. It is particularly about Muslim artists and their way of representing Islam or Islamic cultures in the West.In the last thirty years, neoliberalism through the triumph of free market has influenced the meaning of art by its tendency to commodify almost everything. In this situation, an artist from the periphery has to commodify his/her indigenous culture, which is mainly done by what is called the ‘commodification of difference’. The indigenous artist then is included and entered into the neo-imperialist symbolic order where he/she is not ‘invisible’ anymore.To do this, the artist needs to redefine his/her subjectivity according to the already existing stereotypes; since the ‘core’ could only see what is comprehensible according to its symbolic order. ‘Others’ are simply invisible. I use concepts of stereotype, ambivalence and the anxiety of the colonialist to show that the periphery artist can only be included by accepting and affirming the hierarchical symbolic order of the core (its stereotypes, preconceptions, etc). The picture of a child-like, immature, irrational and savage subaltern is among the most desirable fantasies of the coloniser, especially when it provides the crucial sense of moral superiority.What I argue is that the authenticity of these native artists is only a facade, a mask, to hide the reality of neo-imperialism’s inability of comprehending the ‘Other’. The Other which will remain invisible, without a face, that can only be seen as a ‘bare life’ (in contrast to a ‘political life’) and consequently the subject of humanitarian interventions. <br/

Top-Down Selection in Convolutional Neural Networks

Feedforward information processing fills the role of hierarchical feature encoding, transformation, reduction, and abstraction in a bottom-up manner. This paradigm of information processing is sufficient for task requirements that are satisfied in the one-shot rapid traversal of sensory information through the visual hierarchy. However, some tasks demand higher-order information processing using short-term recurrent, long-range feedback, or other processes. The predictive, corrective, and modulatory information processing in top-down fashion complement the feedforward pass to fulfill many complex task requirements. Convolutional neural networks have recently been successful in addressing some aspects of the feedforward processing. However, the role of top-down processing in such models has not yet been fully understood. We propose a top-down selection framework for convolutional neural networks to address the selective and modulatory nature of top-down processing in vision systems. We examine various aspects of the proposed model in different experimental settings such as object localization, object segmentation, task priming, compact neural representation, and contextual interference reduction. We test the hypothesis that the proposed approach is capable of accomplishing hierarchical feature localization according to task cuing. Additionally, feature modulation using the proposed approach is tested for demanding tasks such as segmentation and iterative parameter fine-tuning. Moreover, the top-down attentional traces are harnessed to enable a more compact neural representation. The experimental achievements support the practical complementary role of the top-down selection mechanisms to the bottom-up feature encoding routines

The effects of controlled language processing on listening comprehension and recall

This study seeks to determine the possible interactions between listening proficiency and the state of strategic self-awareness; second, and more importantly, to investigate the effects of learned strategies on listening comprehension and recall; and finally to describe the most common real-time listening comprehension problems faced by EFL learners and to compare the differences between learners with different listening abilities. After ten training sessions, an assessment was made to see whether or not well-learned strategies could provide students with ample opportunity to practice the comprehension and recall processes. The analyses of the data revealed the causes of ineffective low-level processing and provided insights to solve the problems of parsing. Moreover, the study reveals that explicit instruction of cognitive and metacognitive strategies is needed if a syllabus wishes to help learners improve their listening comprehension and become more-proficient at directing their own learning and development as L2 listeners

Novel Multistage Probabilistic Kernel Modeling in Handwriting Recognition

The design of handwriting recognition systems has been widely investigated in pattern recognition and machine learning literature. It was first attempted to enhance the system's performance by improving the recognition rate to reach $100\%$ which has not achieved yet. Despite the low misclassification error rate, there are still some misclassified test samples. This imposes a very high cost on the whole recognition system. The cost has to be reduced as much as possible which consequently leads to the consideration of reject option to prevent the recognition system from classifying test samples with high prediction uncertainty. The main contribution of this thesis is to propose a novel multistage recognition system that is capable of producing true prediction probability outputs and then reject test samples accordingly. An argument is supported that principally formulated probabilistic classifiers are the best reliable candidates to be utilized in the consideration of reject option. The implementation of reject option based on either non-probabilistic classifier's output score or conversion to probability measures is prone to mistake when compared to an accurate prediction probability output. The Convolutional Neural Network (CNN) is utilized as the automatic feature extractor that can properly harness the spatial correlation of the input raw handwritten images and extract a feature vector with strong discriminative properties. The SVM is used as a powerful classifier to accurately deal with the issue of big data sets. The authentic intuition of extracting the most informative training samples by using the distinguished support vector set from the SVM is also proposed. The Gaussian process classifier (GPC) in the Bayesian nonparametric modeling framework is introduced as the core element of the whole recognition system that can reliably provide an accurate estimate of the posterior probability of the class membership. Experiments under various inference methods, likelihood functions, covariance functions, and learning approaches are conducted in the hope of finding the best model configuration and parameterization. The models are evaluated on two popular handwritten numeral data sets known as MNIST and CENPARMI. The best GPC model in this multistage framework on MNIST can reach $100\%$ reliability rate with the lowest rejection rate of $1.48\%$, the best result achieved in the field. Another inherently probabilistic classifier, known as relevance vector machine (RVM), is also investigated. The RVM is formulated through the sparse Bayesian linear modeling to classification problems and it produces reliable prediction probability outputs. However, In comparison of the GPC with RVM, this argument is experimentally supported that the sparsity is not capable of improving the rejection performance on the data sets

Synthesis of mg/al layered double hydroxide (LDH) nanoplates for efficient removal of nitarate from aqueous solutions

Leaching of nitrate is an important issue on the losses of nitrate from agriculture soils in temperate zone. Decomposition of plants and other organic residues in the soil and improper discharge of sewage lead to the presence of nitrates in the sources of surface and groundwater and flowing water drainage in agricultural drainage networks and their pollution. This study aimed to study the potential use of chloride layered double hydroxide (LDH) nanoplates to remove nitrate from aqueous solutions. The nano-material of chloride-LDH was made by hydrothermal technique and then, its characteristics were specified through scanning electron micrograph and removal of nitrate from aqueous solution by the minerals was investigated in terms of pH, time, speed of shaker, different concentrations of adsorbent and surface adsorption isotherm. Microscopic images of built nanoplates were examined using FESEM and SEM electron microscope with two magnifications. The thickness of nanoplates was about 20nm and their diameter was about 250 nm. Magnified image of the synthesized nanostructures shows squamous-shape. Surface adsorption isotherm of nitrate by chloride- LDH nanoplate was explained with Langmuir model shown with the values greater than 2R. In surface adsorption of nitrate, the optimal values were measured as following: pH = 7, speed = 250 rpm, time = 45 min, concentration of adsorbent = 0.1gr.  This material could adsorb nitrates from aqueous solutions efficiently and effectively.Keywords: pollution, nitrate, layered double hydroxide, hydrothermal, surface adsorptio