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How has targeting helped you to have a better understanding on your state senate universe?, assignment help

How has targeting helped you to have a better understanding on your state senate universe?, assignment help.

Breaking news! Your candidate and campaign have failed to reach the late September fundraising goal and you must reduce your overall budget by \$30,000.However, based on your recent knowledge of targeting, you should now be able to reduce the size of the mail universe in your budget to focus on persuasion.InstructionsPlease rework your mail budget from Week 1 to increase the number of waves of direct mail, but reduce the size of the universe in these mail runs. Post this new budget in the discussion thread below along with brief answers to the following questions:How has targeting helped you to have a better understanding on your state senate universe?How many additional waves of mail can you now send to targeting voters?Based on this new landscape, how do you refocus your budget to deliver the maximum amount of paid media in your budget?
How has targeting helped you to have a better understanding on your state senate universe?, assignment help

MN 207 Purdue Global University Population Parameters Actual Value Discussion.

Unit 9 DiscussionundefinedSubscribeDiscussion OverviewReview the discussion requirements.Sample statistics, such as the sample mean or the sample proportion, can be used to estimate a population parameter (such as the population mean or the population proportion). For example, you can estimate the true mean weight of all full-term, newborn babies in the entire world by collecting a sample and using that sample to generate a 95% confidence interval.Because the sample is typically a relatively small portion of the entire population, errors will have to be considered. Using a sample to create a range or interval of values that estimates a population parameter is called a “confidence interval.”Post 1: Initial ResponseAfter completing your readings for this unit, think about and share your response to the following questions:Offer at least two examples of a population parameter that you cannot calculate, but that you can estimate. Some examples might be the true percentage of the number of bass in Lake Erie or the mean hours of sleep per night for all U.S. college students. In your own words, why do you think it is impossible to know the actual value of any population parameter?A sample can be used to estimate a population parameter. How does the sample size affect the estimate?To estimate a population parameter (such as the population mean or population proportion) using a confidence interval first requires one to calculate the margin of error, E. The value of the margin of error, E, can be calculated using the appropriate formula. The formula depends on whether one is estimating a mean or estimating a proportion.The Margin of Error, E, for a 95% confidence interval for means is: E=1.96sn√ where s is the sample standard deviation and n is the sample size.Invent a quantitative variable, such as age, weight, exam score, etc. Thinking about that variable, create a small set of data (30 data values) to describe that variable. Use Excel to calculate the sample mean of your data and the sample standard deviation. If you create 30 values, the sample size is 30. Then, calculate the margin of error.MM207 Discussion Rubric: 30 pointsStart a New Thread
MN 207 Purdue Global University Population Parameters Actual Value Discussion

National University Obamas Same Path Ad Rhetorical Analysis Paper.

Choose any political ad. Here are two good archives (or you may find the ad anywhere on the Internet as long as you can provide a link):
• Stanford University Political Communication Lab
The Living Room Candidate: Presidential Campaign Commercials 1952-2016
Your task is to do a rhetorical analysis of a political advertisement. In our class, we covered two types of rhetorical analysis—Aristotelian Analysis and Metaphor Analysis. Your paper can focus on one or both of these methods. Use whichever terms help you best explain the rhetorical strategies of the political ad. In your paper’s introduction, you will likely have to put the advertisement in context and explain the rhetorical situation of the ad.
The body paragraphs should break down the ad and reassemble it to make points. Lastly, your paper should have some element of synthesis in it as well. It should incorporate ideas from the following list of class readings.
The conclusion of the paper will likely reflect on whether or not you found the ad rhetorically effective and why.
• Lakoff and Johnson. “Metaphors We Live By.”
• Osborn, Michael. “Archetypal Metaphor in Rhetoric: The Light-Dark Family.”• Geary, James. Excerpt from I is an Other, “Metaphor and Politics.”
• Fowles, Jib. “Advertising’s 15 Basic Appeals.”
• Lakoff, George. “Don’t Think of an Elephant”
National University Obamas Same Path Ad Rhetorical Analysis Paper

Deep Convolutional Neural Models for Image Quality Prediction

Deep Convolutional Neural Models for Image Quality Prediction Introduction: The vital role played by images in human life is manifested by the proverb “A picture is worth thousand words”. The pipelines from picture content generation to consumption are fraught with numerous sources of distortions. Storage and the transmission bandwidth constraints result in induced degradation because of the demand for different compression techniques that reduce storage requirement. Transmission errors and packet losses during communication are other sources that contribute to image distortions. Image processing algorithms used for adopting the changes in resolution, format and color are few more forms of image degradations. Humans can judge image quality almost as a reflex action, but it is impractical to interleave human judgment of image quality as a part of information systems design. Machine evaluation of image quality has been realized as an important area of research in the light of this. Automatic Quality assessment algorithms can be used for optimization purposes, where one maximizes quality at a given cost, for comparative analysis between different alternatives and to benchmark image processing systems and algorithms. Existing Work: Picture-quality models:Picture-quality models are generally classified according to whether a pristine reference image is available for comparison. Full-reference and reduced-reference models assume that a reference is available; otherwise, the model is no-reference, or blind. Reference models are generally deployed when a process is applied to an original image, such as compression or enhancement. No-reference models are applied when the quality of an original image is in question, as in a source inspection process, or when analyzing the image. Generally, no reference prediction is a more difficult problem. No-reference picture-quality models rely heavily on regular models of natural picture statistics [1]. Deep learning and CNNs: Deep learning made breakthrough impact on such difficult problems as speech recognition and image classification, achieving improvements in performance that are significantly superior to those obtained using conventional model-based methods. One of the principal advantages of deep-learning models is the remarkable generalization capabilities that they can acquire when they are trained on large-scale labeled data sets. deep-learning models employ multiple levels of linear and nonlinear transformations to generate highly general data representations [2]. Open-source frameworks such as TensorFlow [3] have also greatly increased the accessibility of deep-learning models, and their application to diverse image processing and analysis problems has greatly expanded. A common conception is that CNNs resemble processing by neurons in visual cortex. This idea largely arises from the observation that, in deep convolutional networks deploying many layers of adaptation on images, early layers of processing often resemble the profiles of low-level cortical neurons in visual area V1, i.e., directionally tuned Gabor filters [4], or neurons in visual area V2 implicated in assembling low-level representations of image structure [5]. At early layers of network abstraction, these perceptual attributes make them appealing tools for adaption to the picture-quality prediction problem. Datasets:The performance of deep-learning models generally depends heavily on the size of the available training data set(s). Currently available legacy, public-domain, subjective picture-quality databases include LIVE IQA [6], TID2013 [7] are relatively small. LIVE IQA contains 29 diverse natural images distorted using five different image distortion types that could occur in real-world applications. The judgments from the subjects are processed and are converted to Difference Mean Opinion Score for each distorted image. The LIVE “In the Wild” Challenge Database [8] with nearly 1,200 unique pictures, each afflicted by a unique, unknown combination of highly diverse authentic distortions and judged by more than 350,000 unique human subjects) is of moderate size. Image recognition data sets such as ImageNet [9] contain tens of millions of labeled images. Common strategies for overcoming this labeled image paucity are data augmentation techniques, which seek to multiply the effective volume of image data via rotations, cropping, and reflections. In another common strategy, the images used for training are divided into many small patches. However, the scores that subjects would apply to a local image patch will generally differ greatly from those applied to the entire image. While generating a large amount of picture content is simple, ensuring adequate distortion diversity and realism is much harder. CNN-based no-reference Image quaity models: Several CNN-based picture-quality prediction models have attempted to use patch-based labeling to increase the set of informative (ground-truth) training samples. Generally, two types of training approaches have been used: patchwise and imagewise, as depicted in Figure 1. In the former, each image patch is independently regressed onto its target. In the latter, the patch features or predicted scores are aggregated or pooled, then regressed onto a single ground-truth subjective score. The first application of a spatial CNN model to the picture quality prediction problem was reported in [11], wherein a high-dimensional input image was directly fed into a shallow CNN model without finding handcrafted features. To obtain more data, each input image was subdivided into small patches as a method of data augmentation, each being assigned the same subjective-quality score during training. Patchwise training was used, and, during application, the predicted patch scores were averaged. Li et al. utilized a deep CNN model that was pretrained on the ImageNet data set [12]. A network-in-network (NiN) structure was used to enhance the abstraction ability of the model. The final layer of the pretrained model was replaced by regression layers, which mapped the learned features onto subjective scores. Image patches were regressed onto identical subjective-quality scores during training. Figure 1. Patchwise and imagewise strategies used to train patch-based picture-quality prediction models [10]. Bosse et al. deployed a deeper, 12-layer CNN model fed only by raw RGB image patches to learn a no-reference picture- quality model [13]. They proposed two training strategies: patchwise training and weighted average patch aggregation, whereby the relative importance of each patch was weighted by training on a subnetwork. The overall loss function was optimized in an end-to-end manner. The authors reported state-of-the-art prediction accuracies on the major synthetic distortion picture-quality databases. To overcome overfitting problems that can arise from a lack of adequate local ground-truth scores, several authors have suggested training deep CNN models in two separate stages: a pretraining stage, using a large number of algorithm-generated proxy ground-truth quality scores, followed by a stage of regression onto a smaller set of subjective scores. For example, [14] describes a two-stage CNN-based no-reference-quality prediction model. The model attains highly competitive prediction accuracy on the legacy data sets. Proposed work: The proposed work aims at designing and implementing a no-reference model for image quality prediction using Deep Convolutional Neural Networks. The works aims at analyzing and comparing the existing methods in terms of the following critical aspects. Strategies to overcome the paucity of large labeled training datasets. Architecture of the deep CNN to be used. The number of stages in training the CNN Aggregation and pooling techniques for better prediction. References [1] A. C. Bovik, “Automatic prediction of perceptual image and video quality,” Proc. IEEE, vol. 101, no. 9, pp. 2008–2024, 2013. [2] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. Advances in Neural Information Processing Systems Conf. 2012, pp. 1097–1105. [3] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, et al., “TensorFlow: Large-scale machine learning on heterogeneous systems.” [Online]. Available: https://www.tensorflow.org/ [4] M. Clark and A. C. Bovik, “Experiments in segmenting texton patterns using localized spatial filters,” Pattern Recognit., vol. 22, no. 6, pp. 707–717, 1989. [5] H. Lee, C. Ekanadham, and A. Y. Ng, “Sparse deep belief net model for visual area V2,” in Proc. Advances in Neural Information Processing Systems Conf., 2008, pp. 873–880. [6] H. Sheikh, M. Sabir, and A. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Process., vol. 15, no. 11, pp. 3440–3451, 2006. [7] N. Ponomarenko, L. Jin, O. Ieremeiev, V. Lukin, K. Egiazarian, J. Astola, B. Vozel, K. Chehdi, et al., “Image database TID2013: Peculiarities, results and perspectives,” Signal Process. Image Commun., vol. 30, pp. 57–77, Jan. 2015. [8] D. Ghadiyaram and A. C. Bovik, “Massive online crowdsourced study of subjective and objective picture quality,” IEEE Trans. Image Process., vol. 25, no. 1, pp. 372–387, 2016. [9] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A largescale hierarchical image database,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009, pp. 248–255. [10] J. Kim, H. Zeng, D. Ghadiyaram, S. Lee, L. Zhang, A. C. Bovik, “Deep convolutional neural models for picture-quality prediction: Challenges and solutions to data-driven image quality assessment” in IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 130-141, 2017. [11] L. Kang, P. Ye, Y. Li, and D. Doermann, “Convolutional neural networks for noreference image quality assessment,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2014, pp. 1733–1740. [12] Y. Li, L. M. Po, L. Feng, and F. Yuan, “No-reference image quality assessment with deep convolutional neural networks,” in Proc. IEEE Int. Conf. Digital Signal Processing, 2016, pp. 685–689. [13] S. Bosse, D. Maniry, T. Wiegand, and W. Samek, “A deep neural network for image quality assessment,” in Proc. IEEE Int. Conf. Image Processing, 2016, pp. 3773–3777. [14] J. Kim and S. Lee, “Fully deep blind image quality predictor,” IEEE J. Sel. Topics Signal Process., vol. 11, no. 1, pp. 206–220, 2017.

Price Mechanism Functions In A Free Market Economy Economics Essay

Question 1 Explain how the price mechanism functions in a free market economy in order to solve the basic economics problem of scarcity. The concept of scarcity in Economics is based on the fact that the human desires are infinite and insatiable and these desires exceed the production of number of products because of limited resources. The needs of businesses, governments and individuals are never satisfied. Usually the products either deprecate or become obsolete with time and there is a need to substitute them. It is argued that this state of being insatiable is basic human nature. Some say that advertisements are an influential factor in generating new needs. This lust is not only limited to new products or services but also to personal life like need for luxury etc. (Arnold, 2001) Resources are fewer in number but the demands are infinite. Price mechanism determines the resource allocation in a free market economic system. Desires of consumers are unlimited but the resources are limited. That is why there is a need to balance the allocation of these resources. Usually pricing is used to determine the allocation of resources in competing uses. Any fluctuation in the demand will result in a fluctuation in supply. Price is used as an indicator. Obviously an increased demand will result in scarcity of the product which will increase the price. The supply of these products is increased to meet the needs of the market. Also this will result in an increase in profits for the producers. Similarly the decrease in demand will result in a decrease in supply. The decrease in demand of a product will result in decrease in the profit margin earned through that specific product. So producers decrease the supply of that product and utilize the resources in production of other products that are more in demand. (Krugman, 2009) Figure 1relationship between demand and price Pricing method is considered advantageous as it allows the allocation of resources more efficiently. This results in technical efficiency as the products are produced at the lowest unit cost. The producers want to produce the products at the nominal costs in the competitive market. The chances of gaining some profit encourage the producers to reduce costs, introduce new products and increase the production of the current products. It is expected that in the long run this phenomenon will result in production of products at lowest unit cost and allocation of resources will be optimal. (Forstater, 2007) Figure 2 A fluctuation in demand affects price as well Allocative efficiency is also adapted by the markets. The demand determines the production or supply of the product. As in the figure above, it can be seen that the increased demand increases the price and a decreased demand decreases the price. Prices are used as indicators to determine where the highest resource allocation is required (usually the products that give highest profit). This creates a unique balance and makes the resource allocation beneficial for everyone. This can be understood by the example of production of laptops. If the demand for laptop increases, laptop manufacturers will increase their productions while the manufacturers of desktop will decrease their production. The production will be according to the demand curve of the market. Which means that the production of most wanted products is increased. Additionally, the market adjusts the changes in demand with the change in supply so that there is no scarcity of any product. (Gwartney, 2005) Question 2 Using diagrams discuss any three types of elasticity with which you are familiar. Explain why they are important. Curve’s elasticity can be defined as the level to which supply or demand curve responds to fluctuation in price. Elasticity of different products is different because of the difference in the demand of the product in the market. Essential products like food and clothing are immune to price changes due to the fact that customers will still buy them regardless of price hikes. These products are considered inelastic. On the other hand if the price of a good product or service that is not essential element of day to day life increases, its consumption will decrease. Such products, whose demand or supply changes with the change in price are highly elastic,. (Arnold, 2001) Elasticity can be calculated by using the equation: Considering the above equation, if elasticity of the curve is lesser than one; it denotes that the curve is inelastic. If it is equal to or more than one, it denotes that the curve is elastic.(Forstater, 2007) The slope of curve of demand is negative. If a slight increase in the price of a product results in a huge decrease in the demand, this will result in a flatter or horizontal demand curve. The flatter curve denotes that the specific product or service is highly elastic. Figure 3 Elastic Demand On the other hand an upright or slightly vertical curve is used to depict an inelastic demand. Figure 4 Inelastic Demand Similarly for supply, for elastic product or service the curve is flat or horizontal. Flatter curve shows that elasticity is greater than or equal to one. Figure 5 Elastic Supply For supply, inelastic curve is represented by an upright or almost vertical curve. Figure 6 Inelastic Supply A. Factors Affecting Demand Elasticity Demand’s price elasticity is affected by the following three factors: (Forstater, 2007) 1. The availability of substitutes – If there are alternatives for a service or product, of course its demand will be more elastic. This means that even a slight increase in the price of a product should result in a decrease in demand of that product. Let’s take a scenario of caffeinated drinks. If there is a price hike of say 50 cents for one cup of coffee. It can be substituted by a cup of tea. This makes coffee an elastic good. On the other hand if the price hike is of caffeine (main ingredient of tea and coffee) rather than of the product, it will result in little or decrease in demand of caffeinated drinks (tea or coffee). As, there are no other alternatives for caffeine, this makes caffeine an inelastic products. If a product is unique meaning having no alternatives, it is considered inelastic. 2. Amount of income available to spend on the good – Demand elasticity is highly dependent on the amount a person can spend on a certain product or service. This means that if the income of a person does not increase but the price of a product increases, the demand of that product will decrease. If the income is stable, then the demand of the product will become elastic. 3. Time – Time is also an important factor that considerably affects the demand elasticity. If there is an increase in price of product say a can of beer. And the consumer finds out he cannot afford to buy 2 or 3 cans of beer at that price in one day. He will reduce the consumption of beer. B. Income Elasticity of Demand The second factor mentioned above states that if income tends to stay the same but the price of the product increases, it will result in a decrease in demand. On the other hand increase in income will result in increase in demand. So income elasticity of demand can be defined as the extent to which the increase in income will result in heightened demand of the product. Following equation shows the income elasticity of demand: ED = Elasticity of Demand Q = Quantity; Y = Income; EDy = Income Elasticity of Demand Demand of an item has high income elasticity if EDy is more than one. Demand is considered income inelastic if EDy is lesser than 0 (Arnold, 2001)

Academy College Latin Americas NACLA Report

Academy College Latin Americas NACLA Report.

Academy College Latin Americas NACLA Report

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