Good Leader Essay Example | Topics and Well Written Essays - 1000 words

Good Leader - Essay Example However, all leaders possess certain common qualities that help them to perform their role. If an organization has proper leaders with a vision that they can articulate and implement in various situations, their management is considered to be well-organized (Hesburgh, N.D). These are the essentials for an organization to succeed and reach new heights. The position of the managers, supervisor, lead etc. does not make a leader; it simply makes you the boss. A boss is the one who just assigns tasks for everyone; while a leader motivates each and everyone to achieve those goals and make them want to do a certain task given (Hakala, 2008). Leaders also do not restrict the employees or people working under them to follow the certain method prescribed by them; they give them a chance to brainstorm and use a method that is mutually beneficial for them and the organization. This augments the intellectual capabilities of the employees and the thrill of choosing to do whatever method they want, as far as it is appropriate, motivates them and increases not only their efficiency and productivity, but also their devotion and commitment to the organization. No doubt, a company is run by the company heads, CEOs, Managing Directors followed by general managers or department heads, but the work that is to be performed is to be carried out by the e mployees to a very large extent. Therefore, once their confidence and loyalty for the company is gained with enough stimulation within them, a company can run very smoothly and flourish in the future. The core functions of management are planning, organizing, leading and controlling. Planning is to develop and design the goals of the company depending upon the objectives; organizing is to divide and assign tasks to every worker depending upon his rank; leading is to monitor employees and motivate them in different capacities required; and controlling is to bring about the change required within the management for the improvement of the company's performance. Thus, we can judge that leadership is a very crucial part of management. A company can be well-structured and reap profits if proficient leadership is available. It is a concept that good management is to resolve problems; however, good management is to be able to resolve problems in such a way that they are prevented in the future (Reh, 2006). If each problem is tackled following this pattern, an unsuccessful business can surely transform into a successful one. A good leader is expected to have certain qualities that benefit the organization directly or indirectly. Integrity is an important quality of a leader - the inner values and the outward actions are the same, that is, there is no hypocrisy involved. This will result in honest dealings that will augment the goodwill of a company, making it popular. A leader needs to be dedicated to his company; his passion and commitment to the company will motivate him to make arduous efforts for the enhancement of the company. Magnanimity is critical because the leader tends to give the credit for a good performance to the deserving person. Such rewards and acknowledgements becomes and incentive for the employees to struggle and work harder, indirectly benefiting the company. A leader should be down to earth and should have humility - this will prevent him to self-centered and

Integrating Four Skills in Clt Context Essay Example for Free

Integrating Four Skills in Clt Context Essay In the history of language teaching, many methods and approaches have been used and developed. Among them Communicative Language Teaching is now thought as the most influential or must-use one. It is clearly defined in the handout: It would be fair to say that if there is any one umbrella approach to language teaching that has become the accepted norm in this field, it would have to be the Communicative Language Teaching Approach. Below is a lesson plan which is based on the CLT approach and we try to integrate four macro skills in it. This lesson plan is designed or intermediate-level or sophomore students at SFLC, NUM. In the lesson, we are aiming at a result in which students are actually able to use the language about environment, especially about the climate change/global warming, both verbally and in writing. Even though it looks like each task focuses on different skill, every task is designed to integrate at least 2 skills of the four. Also, the lesson is a part of a sequence lesson which means in the previous class they have been introduced vocabulary related to the environment. The period of the lesson plan is an hour and a half. Lesson procedure Warm-up: As a warm-up students are asked about the weather that day and previous day. The question is for the whole class; therefore anyone who feels comfortable to talk in front of the whole class can answer. Then they are asked to work in pairs to talk about their favorite weather. Its a simple task to encourage the students and build confidence in them. The warm-up is about 10-15 minutes. Speaking: When the students are thought to be ready and confident enough, gradually turn the class into a discussion about climate change and global warming (causes of the climate hanges, whats happening in the world, what we can do about it, etc). Then they are divided into group of three and given statements of the Task 1 . Students should discuss about the statement and when they are finished with it they can rotate the statements around the groups. Since the topic is a popular one all the students are encouraged to speak out. They can also use some Mongolian language if they cant express them freely (but they should try as much English as they can) . As for the teacher, they need to use the target language as often as possible in order to provide ealistic models for students to use. I would give some explanation or help in Mongolian on the students request (15-20 minutes). Listening: As the discussion is on the global warming and climate change, students are now asked to listen to people speaking out their opinion on global warming. This is very authentic and contextualized because the tape script is actually taken from a readers discussion on a weather website. It will help the students to move from structured language production to more communicative language use . At first the tape is played without ny interference. For the second time it should be stopped after each speaker and the students are asked if they agree or disagree with that person and to comment on the speakers opinion. If the students do not agree with each other and start an argument, they should be encouraged to defend their own idea. In this way, the listening task would turn from an inactive listening task to one where the students take alternate roles as listener and speaker (20 minutes). Writing: At this time, own opinion and built knowledge on the subject . Therefore, students are asked to rite a small opinion passage about global warming. They should imagine that they are writing it on the real website discussion. In the previous task students have listened texts which models how people are expressing their opinion. Thus, the students will not face difficulties. After they are done with the task, they should exchange what they have written (20 minutes). Reading: Students are given handouts in which they need to read short passages about different areas of environmental issues (preferably questions discussed in the speaking section). Students are asked o read the passages at their own speed and match them to a set of questions. In order to match questions to the passages, students are required to demonstrate a global comprehension of the passages. Also this type of readings sharpens the students reading skills. While reading the handouts students are not allowed to use dictionary, because it distracts them from understanding the text. At the same time, it will help them to learn to recognize words automatically which is a basis for reading skill . When students complete the task, check the answer. (1 5 minutes) Homework (explaining HW will take 5 minutes): Students are asked to work in groups of four. Choose one of the environmental issues and present it to the class. It can be in any form e. g. ews report, role-play, informative presentation and etc. This task is supposed to integrate all four skills including (but not limited to) activities such as: 0 Doing some research and reading them thoroughly 0 Or listening to news reports for information 0 Writing about the topic based on information they found 0 Finding related pictures and other visual materials and preparing to explain them Working with their partners which should include sharing opinions and listening to the others 0 Presenting it to the class and responding to any questions those come from peer students afterwards. Handouts for the tasks Task 1: Discussion Statements Climate change is the most serious threat to our planet at the moment. All countries should be forced to apply serious regulations to reduce carbon emissions. Normal people cant do much to stop global warming. I am worried about climate change. Everybody should do whatever they can to save energy. Climate change isnt as serious as people say. People like to worry about something! There are simply too many people living on planet earth! We are going to lose many animal species and areas of low land in the very near future be cause of global warming. Gonzalo Im all in favour of global warming. I grow tropical plants so for me the warmer the weather is the better! Tanya In 20 years time the traditional British weather will be a thing of the past. Well have a climate like the south of France. People will be healthier as theyll spend more time outdoors. Just think, dining al fresco in the summer months. Itll be great! Luis no one can tell me that global warming isnt happening. Weve Just had the hottest year on record! My sister lives in the north of Spain and she said that it is beach weather there and its November. I mean its not normal is it? Kevin When I was a boy we used to have heavy snow most years. Since the early 90s all weve had is a light dusting of snow. It must be due to global warming. Ruth You only have to switch on the news to see the crazy things the weather is doing. There are so many floods, hurricanes and droughts. Its the extreme weather conditions caused by global warming. Oliver Theres no such thing as global warming. Its all media hype to brainwash people. If they told us the moon was made of cheese often enough people would believe it! Mark The world will never be the same again, but thats how it has always been. It changes constantly and nature and man can adapt to these changes. If we couldnt, human life on the planet would have finished years ago. Task 3 Who do you agree with most? Who do you disagree with? What would you write to the message board? Put your message here. Share your message with the class. Does anyone have the same view as you?

Image Super-Resolver using Cascaded Linear Regression

Image Super-Resolver using Cascaded Linear Regression Abstract A number of existing super-resolution algorithms fail in modeling the relationship between high and low resolution image patches and time complexity in training the model. To overcome the above-stated problem, simple, effective, robust and fast image super-resolver (SERF) based on cascaded linear regression has been used for learning the model parameters. The image divided into patches are grouped into clusters using k-means clustering algorithm for learning the model parameter based on series of linear least square function, named cascaded linear regression to identify the missing detail information. This approach has been simulated using MATLAB for various images. The simulation results show that SERF gives better PSNR and less computation cost compared to existing methods. Keywords-Cascaded linear regression, example learning based image super-resolution, K-means. Super-Resolution (SR) is the process of producing a high-resolution (HR) image or video from low-resolution images or frames. In this technology, multiple low-resolution (LR) images are applied to generate the single high-resolution image. The image super-resolution is applied in a wide range, including the areas of military, medicine, public safety and computer vision, all of which will be in great need of this technology. The SR process is an ill-posed inverse problem, even though the estimation of HR image from LR input image has many possible solutions. There are many SR algorithms available to resolve this ill-pose problem. Interpolation Based method is the most intuitive method for the image super-resolution. This kind of algorithm has the low-resolution image registered on the grid of the high-resolution image to be calculated. Reconstruction based method is mainly based on iterative back projection method. This algorithm is very convergent, simple and direct, but the resoluti on is not steady and unique. Because of the limitation of the reconstruction algorithm, the learning-based super-resolution technology emerges as an active research area. Learning based approach synthesize HR image from a training set of HR and LR image pairs. This approach commonly works on the image patches (Equal-sized patches which is divided from the original image with overlaps between neighbouring patches). Since, learning based method achieves good performance result for HR image recovery; most of the recent technologies follow this methodology. Freeman et al [1] describe a learning based method for low-level vision problem-estimating scenes from images and modeling the relation between synthetic world of images and its corresponding images with markov network. This technique use Bayesian belief propagation to find out a local maximum of the posterior probability for the scene of given image. This method shows the benefits of applying machine learning network and large datasets to the problem of visual interpretation. Sun et al [2] use the Bayesian approach to image hallucination where HR images are hallucinated from a generic LR images using a set of training images. For practical applications, the robustness of this Bayesian approach produces an inaccurate PSF. To overcome the estimation of PSF, Wang et al [3] propose a framework. It is based on annealed Gibbs sampling method. This framework utilized both SR reconstruction constraint and a patch based image synthesis constraint in a general probabilistic and also has poten tial to reduce the other low-level vision related problems. A new approach introduced by Yang et al [4] to represent single image super-resolution via sparse representation. With the help of low resolution input image sparse model, output high resolution image can be generated. This method is superior to patch-based super-resolution method [3]. Zedye et al [5] proposed a sparse representation model for single image scale-up problem. This method reduces the computational complexity and algorithmic architecture than Zhan [6] model. Gao et al [7] introduce the sparsity based single image super-resolution by proposing a structure prior based sparse representation. But, this model lags in estimation of model parameter and sparse representation. Freedman et al [8] extend the existing example-based learning framework for up-scaling of single image super-resolution. This extended method follows a local similarity assumption on images and extract localized region from input image. This techn ique retains the quality of image while reducing the nearest-neighbour search time. Some recent techniques for single image SR learn a mapping from LR domain to HR domain through regression operation. Inspired by the concept of regression [9], Kim [10] and Ni Nguyen [11] use the regression model for estimating the missing detail information to resolve SR problem. Yang and Wang [12] presented a self-learning approach for SR, which advance support vector regression (SVR) with image sparse co-efficient to make the model relationship between LR and HR domain. This method follows bayes decision theory for selecting the optimal SVR model which produces the minimum SR reconstruction error Kim and Kwon [13] proposed kernel ridge regression (KRR) to train the model parameter for single image SR. He and siu [14] presented a model which estimates the parameter using Gaussian process regression (GPR).Some efforts have been taken to reduce the time complexity. Timofte et al [15] proposed Anchored neighbourhood regression (ANR) with projection matrices for mapping the LR image patches onto the HR image patches. Yang et al [16] combined two fundamental SR approaches-learning from datasets and l earning from self-examples. The effect of noise and visual artifacts are suppressed by combining the regression on multiple in-place examples for better estimation. Dong et al [17] [18] proposed a deep learning convolutional neural network (CNN) to model the relationship between LR and HR images. This model performs end-to-end mapping which formulates the non-linear mapping and jointly optimize the number of layers. An important issues of the example learning based image SR technique are how to model the mapping relationship between LR and HR image patches; most existing models either hard to diverse natural images or consume a lot of time to train the model parameters. The existing regression functions cannot model the complicated mapping relationship between LR and HR images. Considering this problem, we have developed a new image super-resolver for single image SR which consisting of cascaded linear regression (series of linear regression) function. In this method, first the images are subdivided into equal-sized image patches and these image patches are grouped into clusters during training phase. Then, each clusters learned with model parameter by a series of linear regression, thereby reducing the gap of missing detail information. Linear regression produces a closed-form solution which makes the proposed method simple and efficient. The paper is organized as follows. Section II describes a series of linear regression, results are discussed in section III and section IV concludes the paper. Inspired by the concept of linear regression method for face detection [19], a series of linear regression framework is used for image super-resolution. Here, the framework of cascaded linear regression in and how to use it for image SR were explained. A. Series of Linear Regression Framework The main idea behind cascaded linear regression is to learn a set of linear regression function for each cluster thereby gradually decreasing the gaps of high frequency details between the estimated HR image patches and the ground truth image patches. In order to produce the original HR image from LR input image, first interpolate LR image to obtain the interpolated LR image with same size as HR image. This method works at the patch level, each linear regressor parameter computes an increment from a previous image patch, and the present image patch is then updated in cascaded manner. (1) (2) denotes the estimated image patch after t-stages. denotes the estimated increment. denotes feature extractor by which the f-dimensional feature vector can be obtained. Linear regressor parameters at t-stage T Total number of regression stages. The next step is learning of the linear regression parameters and for T stages. Relying on these linear regression T stages, parameters for regressors are subsequently learnt to reduce the total number of reconstruction errors and to make presently updated image patch more appropriate to generate the HR patch. Using least squares form to optimize and , it can be written as, (3) The regularization term accomplishes a constraint on the linear regression parameters and to avert over-fitting and ÃŽÂ ² be the data fidelity term and the regularization term. At each regression stage, a new dataset values can be created by recurrently applying the update rule in (1) with learnedand. Next, and can be learned subsequently using (2) in cascade manner. Fig. 1. Flow of cascaded linear regression framework B. Pseudo code For Cascaded Linear Regression Algorithm The Pseudo code for cascaded linear regression algorithm for training phase is given below, Input: , image patch size à ¢Ã‹â€ Ã… ¡d xà ¢Ã‹â€ Ã… ¡d for t=1 to T do { Apply k-means to obtain cluster centres for i = 1 to c do { compute A and b. update the values of A and b in . } end for } end for The output of this training phase is and cluster centroid. C. SERF Image Super-Resolver This section deals with cascaded linear regression based SERF image. The process starts by converting color image from the RGB space into the YCbCr space where the Y channel represents luminance, and the Cb and Cr channels represent the chromaticity. SERF is only applied to the Y channel. The Cb and Cr channels reflect G and B channels of the interpolated LR image. D. SERF Implementation To extract the high frequency details from each patch by subtracting the mean value from each patch as feature patch denoted as . Since the frequency content is missing from the initially estimated image patches, the goal of a series of linear regression is to compensate for high frequency detail (4), (4) To diminish the error between HR feature patch and the estimated feature patch, it is normal that the regression output should be small. Hence, by putting the constraint on regularization term to (4), the output is, (5) Where, ÃŽÂ » is the regularization parameter. t Denotes the number of regression stages. denotes the feature extractor. ÃŽÂ ² and ÃŽÂ » are set to 1 and 0.25. A closed-form solution for equation (5) can be computed by making the partial derivative of equation (5) equal to zero. In testing phase, for a given LR image, bicubic interpolation is applied to up sample it by a factor of r. This interpolated image is divided into M image patches. Feature patches are calculated by subtracting the mean value from each image patch. At the tth stage, each feature patch is assigned to a cluster l according to the Euclidean distance. To obtain the feature subsequently, linear regression parameters are applied to compute the increment. Concurrently, the feature patch is updated using, (6) After passing through T-stages, reconstructed image patches are obtained by adding mean value to the final feature patches. All the reconstructed patches are then combined with the overlapping area and then averaged to generate the original HR image. E. Pseudo code For SERF Image Super-Resolver Algorithm The pseudo code for SERF image super-resolver algorithm is as follows: Inputs: Y, a, r, for t=1 to T do { Adapt each patch clusterto a cluster. Compute. Update the values of A and b in } End for The output will be the High Resolution image (HR). The simulation of the SERF image super-resolver is done by using MATLAB R2013a for various images. The LR image is read from image folder and is processed using the algorithms explained before. The output HR image is taken after regression stages. The implementation is done by considering many reference images. The colour image (RGB) is first converted into YCbCr space, where Y channel represents luminance. Cb and Cr are simply copied from the interpolated LR image. The number of cluster size is 200. Image patch size 5 x 5 and magnification factor is set to 3. a)LR input b)HR input (c)Zooming result Fig.2. SERF Result under Magnification Factor 3 a)LR input b)HR output c)zooming result Fig.3. SERF Result under Magnification Factor 2 a)LR input b)HR output c)zooming result Fig.4. SERF Result under Magnification Factor 1 (a) (b) (c) (d) (e) (f) (g) (h) Fig.5. Comparisons ResultsButterfly (a) ground truth image (original size is 256 ÃÆ'- 256); (a)super-resolution results of (b) SRCNN, (c) ScSR, (d) Zeydes, (e) ANR, (f) BPJDL,(g) SPM, and (h) SERF. Zeydes [5] method gives noiseless image, but texture details are not well reconstructed as shown Figure (d). The BPJDL [14] methods generate sharper edges when compared to other methods as shown Figure (f). Figure (h) shows the zooming results of SERF method that performs well for both reconstruction and visual artifacts suppression. TABLE I:PSNR AND SSIM VALUES UNDER MAGNIFICATION FACTOR OF 1, 2 AND 3. Magnification Factor PSNR SSIM TIME(s) 3 29.0775 0.839 0.4323 2 30.5 0.812 0.4000 1 38.4 0.798 0.3870 TABLE II:PSNR AND SSIM VALUES UNDER MAGNIFICATION FACTOR OF 3 FOR TESTING IMAGES. S.NO IMAGES PSNR SSIM TIME(s) 1 Baboon 23.63 0.532 0.3115 2 Baby 35.29 0.906 0.4148 3 Butterfly 26.87 0.883 0.2018 4 Comic 24.32 0.755 0.2208 5 Man 28.19 0. 778 0.5468 6 zebra 29.09 0.839 0.4324 For magnification factor of 3, SERF outplays ScSR method by an average PSNR gain of 0.43dB, Zeydes [5] method by 0.37dB, ANR [15] by 0.44dB, BPJDL [14] method by 0.23dB and the SPM [7] method by 0.16dB. SERF gives average SSIM value of 0.8352 and it is fastest method compared to existing methods (TABLE III). TABLE III: PSNR AND SSIM VALUE COMPARISON OF SERF METHOD WITH EXISTING METHODS UNDER MAGNIFICATION FACTOR OF 3. EXISTING METHODS PSNR SSIM TIME(s) ScSR [4] 23.69 0.8835 7.27 Zeydes [5] 23.60 0.8765 0.06 ANR [15] 24.32 0.8687 0.02 BPJDL [14] 24.17 0.8890 17.85 SPM [7] 24.63 0.8982 0.74 SERF 29.0775 0.8352 0.23 SERF has few parameters to control the model, and results in easy adaption for training a new model when the experimental settings, zooming factors and databases were changed. The cascaded linear regression algorithm and SERF image super-resolver has been simulated in MATLAB2013a. SERF Image super-resolver achieves better performance with sharper details for magnification factor up to 3. This model reduces the gaps of high-frequency details between the HR image patch and the LR image patch gradually and thus recovers the HR image in a cascaded manner. This cascading process promises the convergence of SERF image super-resolver. This method can also be applied to other heterogeneous image transformation fields such as face sketch photo synthesis. Further this algorithm will be implemented on FPGA by proposing suitable VLSI architectures. REFERENCES [1] W. Freeman, E. Pasztor, and O. Carmichael, Learning low-level vision, International Journal of Computer Vision, vol. 40, no. 1, pp. 25-47,2000. [2] J. Sun, N. Zheng, H. Tao, and H. Shum, Image hallucination with primal sketch priors, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2003, pp. 729-736. [3] Q. Wang, X. Tang, and H. Shum, Patch based blind image super resolution, in Proceedings of IEEE international Conference on Computer Vision, 2005, pp. 709-716. [4] J. Yang, J. Wright, T. Huang, and Y. Ma, Image super-resolution via sparse representation, IEEE Transactions on Image Processing, vol. 19,no. 11, pp. 2861-2873,2010. [5] R. Zeyde, M. Elad, and M. Protter, On single image scale-up using sparse-representations, in Proceedings of Curves and Surfaces, 2012, pp. 711-730. [6] X. Gao, K. Zhang, D. Tao, and X. Li, Joint learning for single-image super-resolution via a coupled constraint, IEEE Transactions on Image Processing, vol. 21, no. 2, pp. 469-480, 2012. [7] K. Zhang, X. Gao, D. Tao, and X. Li, Single image super-resolution with multiscale similarity learning, IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 10, pp. 1648-1659, 2013. [8] G. Freedman and G. Fattal, Image and video upscaling from local selfexamples, ACM Transactions on Graphics, vol. 28, no. 3, pp. 1-10, 2011. [9] K. Zhang, D. Tao, X. Gao, X. Li, and Z. Xiong, Learning multiple linear mappings for efficient single image super-resolution, IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 846-861, 2015. [10] K. Kim, D. Kim, and J. Kim, Example-based learning for image super resolution, in Proceedings of Tsinghua-KAIST Joint Workshop Pattern Recognition, 2004, pp. 140-148. [11] K. Zhang, D. Tao, X. Gao, X. Li, and Z. Xiong, Learning multiple linear mappings for efficient single image super-resolution, IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 846-861, 2015. [12] M. Yang and Y. Wang, A self-learning approach to single image super resolution, IEEE Transactions on Multimedia, vol. 15, no. 3, pp. 498-508, 2013. [13] K. Kim and K. Younghee, Single-image super-resolution using sparse regression and natural image prior, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, 2010. [14] H. He and W. Siu, Single image super-resolution using gaussian process regression, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 449-456. [15] R. Timofte, V. Smet, and L. Gool, Anchored neighborhood regression for fast example-based super-resolution, in Proceedings of IEEE Conference on Computer Vision, 2013, pp. 1920-1927. [16] J. Yang, Z. Lin, and S. Cohen, Fast image super-resolution based on in-place example regression, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1059-1066. [17] C. Dong, C. Loy, K. He, and X. Tang, Learning a deep convolutional network for image super-resolution, in Proceedings of European Conference on Computer Vision, 2014, pp. 184-199. [18] C. Dong, C. Loy, K. He, and X. Tang, Image super-resolution using deep convolutional networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI:10.1109/TPAMI.2015.2439281, 2015. [19] P. Viola and M. Jones, Robust real-time face detection, International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.

Elegy, Written With His Own Hand In the Tower Before His Execution and

Comparing Metaphors in Chidiock Tichborne's Elegy, Written With His Own Hand In the Tower Before His Execution and William Blake's To See A World In A Grain Of Sand Chidiock Tichborne's "Elegy, Written With His Own Hand In the Tower Before His Execution" and William Blake's "To See A World In A Grain Of Sand" contain several fascinating metaphors that produce two impressive verses that capture our imagination. Both of these elegies deal with life and the contrasting ways it surfaces from within the poet's mind. These metaphors (an analogy between two things that give us insight into the unrevealed part) create an image within our minds that maintain our attention throughout the verse. In Blake's, "To See A World In A Grain Of Sand," every line is a metaphor that secures our attention and blazes our imagination. Blake expresses a metaphor wisely when he asserts "†¦Hold infinity in the palm of your hand†¦" (Blake 125, line 3). Humans have always grasped onto time, as if by gripping it tightly, we can control its outcome: multiply time, making time stand still, and so forth. Blake...

My Favourite Peson Essay

My mother is the mostconfusing, weird, insane, and strangest person i know; she is also the most beautiful, caring, loving, and strongest figure in my life. She loves learning, and has been supportive in my diseases. She has taught me so many things about a natural lifestyles that I keep in mind day to day. I have manyinspirational people that surround me, but my mother’s crazy, creative, and supporting personality has made her my favorite person in the world. Just sitting with her brings me comfort. She smells of sweet coconut and her skin is warm and soft like soft cashmere. I love her laugh, and how her eyes shine optimistically and full of life. She has a way of making those crazy and stressful days melt away and leaves your soul content. Even when others speak ill of her, I never hear her returning such words. Right now she is miles away, yet I call her and her voice is as warm as her embrace. There is people in this world who deserve an award, and she is one of them. The way she has grown in the past few years astounds me. I can see it in her actions that she is stronger than i have ever seen her, and each time i see her she looks more beautiful than she did before. She is no ordinary woman. She dances around the house with the grace of a fish out of water, and she gets away with it. She remembers only parts of songs and movies, I found it irritating; but now all I want is to hear her sing her fragmented songs and dance all the time. My favorite memory of her is playing card games, we would play using weird accents that must be a cross between Russian, and Icelandic. I’m not sure anyone else could understand us, and frankly I am pretty sure someone would send us to Essandale if they were to see us. Particularly when we cooked a meal together and turned on the music, then the magic would really happen. Those are the memories I hold dear.I applaud my mother for her strength. My family tells me that I am like her, and if I am anything like her than I would not complain. Having a child at 18 can’t be easy, growing up I know she always tried her hardest to be thebest mother she could be. Having a child with 3 diseases must have taken a pretty big toll on her, yet she learned all she  could about them and provided the supportive lifestyle I needed to not let these diseases take control of me. She always tries to make the best out of a bad situation, and she has known her fare share of that. Even with battles of her own she would wipe her tears away and take away my sorrows. She has taught me so much, one thing I appreciate the most is her interest in natural products. Her interest has also brought a great many memories that I will never forget. She was starting to get into natural shampoo products,I believe she tried washing her hair with eggs and rinsing it with baking soda. She came out with her hair looking like Frankenstein, it really did feel like wire. She even tried to put oil in it to fix it, it made no difference. My mom has tried so many things, although many of her attempts did not work as planned she never stopped. Among one of her attempts was a body wash, it ended up as a lumpy concoction that looked that like glue and tapioca beads. She went on to create my favorite rejuvenating body wash with fresh mint and sweet thyme, and every time I smell it think of her. She has so many books on natural home remedies, that I am very thankful for. But the way she studies for hours and keeps going is truly epic, she is always eager to learn more and i s very determined. Through life we learn lessons, most of them are through people we meet and situations we endeavour. I think its the small things learned that make the difference in life. I learned so many things from her, and together they have made me who I am today. She may not be the most patient at times, but she has been very patient with me. My mother is my favorite person. They say a parents love just happens, I believe different. She has carved herself into my heart with a silken blade. Love and Respect cannot just be forced upon a person, but is a process like any relationship that is done with patience, acceptance, and frustration. I know I will be successful in my life, because of her. I have learned never to give up, even when the world is against you. Because of her compassion and forgiveness , I have come to know a lust for life and to feel content even when the worlds a blur.

Why Fairy Tail Is a Bad Manga

Why Fairy Tail is a Bad Manga One of the most popular manga in circulation today is Fairy Tail. It is about a wizard guild named Fairy Tail, and the adventures of two of its members, a boy named Natsu and a girl named Lucy. In all, it seems like it could be a good story, but the writer could just not pull it off. Fairy Tail has one of the worst plots and character backgrounds of all the mangas still going today. To start off, we can review the problems with the main characters. Natsu, a boy who uses â€Å"Dragon-slayer† magic was found in the woods and brought up by a dragon.The dragon taught him too read, speak, and learn a secret technique that could be used too kill dragons! Not only does the author, Mashima Hiro, ruins the character background with an almost alternate version of â€Å"Tarzan† he also screws up the girl, Lucy, role in the story. She can’t really do anything. She just has a set of keys that summon magical spirits that she sends to fight for her . But, usually they are useless since she can only use each key on certain days. Her spirits also hardly ever follow her directions, so her whole character I completely useless.Her only real contribution too the story, is the comical situations that she is put in, and her figure, which consists of blond hair, brown eyes, and large breasts, which add sex appeal. Natsu on the other hand gains the ability to â€Å"eat† and control fire, from his â€Å"dragon-slayer† magic. The next problem in the story is the character development. In most well written mangas, the main characters either, mature or get considerably stronger while the story goes on. Yet, in for Fairy Tail, this is not the case. Instead, the main character remains the exact same way throughout, most of the story.For instance, Natsu, only gets stronger for brief periods of time every now and then, before going back too his normal level. Lucy acquires more keys, yet is still at the same level she started off a s, because her spirits hardly ever do anything right so she still ends up becoming useless. In other famous manga, the main sometimes, goes off for a couple years in the story, too do some kind of intense training, then comes back, extremely strong, like One Piece or Naruto. However in Fairy Tail the main characters disappear for seven whole years, and still don’t get stronger, as they were supposedly frozen in time!Finally, we have to look at the emotional aspect. Mashima Hiro, fails too bring out emotion in the reader. Whenever a bad something bad happens, it is almost immediately resolved. For instance, in volume 25 chapter 257, Lucy finds out her father has died, after the time skip. She starts crying, but not three pages later, gets over it, and goes on another adventure. Fairy Tails only good point in the whole manga, is its entertainment factor. If anything, it is funny and again, has a certain sex appeal, since most of the women in the manga have large breasts and wea r revealing clothing.It has a more upbeat kind of theme, were the villains practically say, â€Å"O well. You beat me too a pulp. I have now found the error in my ways and am going too join you/be good. † Besides its entertainment factor, there is no way; Fairy Tail would be as popular as it is now. The character development majorly lacks, the backgrounds of different characters are unsophisticated, and the author fails at creating emotion. So if you enjoy a manga, with a good plot and story line, please do not choose to read Fairy Tail, as you will be extremely disappointed.