How to keep your body in a good shape Essay Example | Topics and Well Written Essays - 500 words

How to keep your body in a good shape - Essay Example However, one should only read that which has substantial depth and those that have mind stimulating qualities. It is also crucial to take time off cognitive activities to improve memory and other cognitive skills. This is concerning taking beaks and recesses from time to time, as they help in building one’s retention and attention span. This way, cognitive skills such as critical and logical thinking remain sharp at all times and boosts quick learning. This works hand in hand with improved task coordination, concentration, and planning. Physically, it is crucial to keep the body in good shape through physical exercises as they increase the flow of blood in the body. The first and easiest way to keep one’s body physically in shape is taking a walk in the yard or walking the dog, if you have one (Templeton). Moreover, Yoga and tai chi are good techniques for keeping the mind in motion and stretching respectively. These physical exercises not only keep the mind and blood flow active, but also ward off certain illnesses, which include Alzheimers and dementia, as well as signs of aging. This occurs due to the relationship between the brain and the cardiovascular system. The use of drugs has profound effects on our bodies concerning maintaining health. As a result, to remain in good shape it is in our best interests to control our ailments such as colds and other personality disorders without the use of drugs or medication. Because of this, the body does not develop dependency on the drugs and medication meaning that one can act and live normally in their absence. In addition, failure to use drugs cuts back on the risk of developing resistance for mainstream medication thus, boosting health. In addition, at times, drugs and medication result in treating symptoms and not the condition that one suffers from resulting in worsening of the condition. Finally, in case of safety, one should avoid

Detecting Complex Image Data Using Data Mining Techniques

Detecting Complex Image Data Using Data Mining Techniques Detecting complex image data using data mining techniques IMRAN KHAN ABSTRACT The Internet, computer networks and information are vital resources of current information trend and their protection has increased in importance in current existence. The intrusion detection system (IDS) plays a vital role to monitors vulnerabilities in network and generates alerts when found attacks. Today the educational network services increasing day today so that IDS becomes essential for security on internet. The Intrusion data classification and detection process is very complex process in network security. In current network security scenario various types of Intrusion attack are available some are known attack and some are unknown attack. The attack of know Intrusion detection used some well know technique such as signature based technique and rule based technique. In case of unknown Intrusion attack of attack detection is various challenging task. In current trend of Intrusion detection used some data mining technique such as classification and clustering. The process of c lassification improves the process of detection of Intrusion. In this dissertation used graph based technique for Intrusion classification and detection. This dissertation proposes efficient intrusion detection architecture which named IDS using improved ensemble techniques (IDSIET). The IDSIET contains a new improved algorithm of attribute reduction which combines rough set theory and a method of establishing multiple rough classifications and a process of identifying intrusion data. The experimental results illustrate the effectiveness of proposed architecture. Our proposed work is implemented in MATLAB .for implementation purpose write various function and script file for implementation of our proposed architecture. For the test of our hybrid method, we used DARPA KDDCUP99 dataset. This data set is basically set of network intrusion and host intrusion data. This data provided by UCI machine learning website. Proposed method compare with exiting ensemble techniques and generate the improved ensemble technique to getting better result such as detection rate, precision and recall value. Keywords- Intrusion Detection System (IDS), IDSIET, Neural Network, rough set theory, Network Security, MATALAB, KDDCUP99 Dataset. PROPOSED METHODOLOGY AND ARCHITECTURE Comparison with linear scale-space representation While not being used explicitly in SURF, we take interest here in the approximation of Gaussian kernels by box filters to understand the advantages and the limitations of the SURF approach. 3.1 Scale-space representation linear scale space The linear scale-space representation of a real valued image u : R2 7→ R defined on a continuous domain is obtained by a convolution with the Gaussian kernel uÏÆ' := GÏÆ' âˆâ€"u (1) where GÏÆ' is the centered, isotropic and separable 2-D Gaussian kernel with variance ÏÆ'2 ∀(x,y) ∈R2, GÏÆ'(x,y) := 1 2Ï€ÏÆ'2 e−x2+y2 2ÏÆ'2 = gÏÆ'(x)gÏÆ'(y) and gÏÆ'(x) = 1 √2π ·ÃÆ'e− x2 2ÏÆ'2 . (2) The variable ÏÆ' is usually referred to as the scale parameter. Discrete scale space In practice, for the processing of a numerical image u, this continuous filter is approximated using regular sampling, truncation and normalization: ∀i,j ∈J−K,KK GÏÆ'(i,j) = 1 CK GÏÆ'(i,j) , where CK = K Xi,j =−K GÏÆ'(i,j). (3) The scale variable ÏÆ' is also sampled, generally using a power law, as discussed later in  § 3.2. Discrete box space Making use of the aforementioned box filter technique, such a multi-scale representation can be (very roughly) approximated using a box filter with square domain Γ = J−Î ³,ÃŽ ³KÃâ€"J−Î ³,ÃŽ ³K uÃŽ ³ := 1 (2ÃŽ ³ + 1)2 BΓ âˆâ€"u. (4) The question now is how to set the parameter ÃŽ ³ ∈ N to get the best approximation of Gaussian zoom-out. Second moment comparison One may for instance choose to match the second order moment ÏÆ'2 of the 1D Gaussian gÏÆ' and the variance of the corresponding box filter, as suggested by [7]. This leads to the relation ÏÆ'2 ÃŽ ³ = ÃŽ ³ Xi =−Î ³ i2 2ÃŽ ³ + 1 = (2ÃŽ ³ + 1)2 −1 12 = ÃŽ ³(ÃŽ ³ + 1) 3 , (5) where ÏÆ'2 ÃŽ ³ is the variance of the centered 1D box filter with width 2ÃŽ ³ + 1. Thus, for large values of filter size (ÃŽ ³ 1), we get approximately ÏÆ'ÃŽ ³ ≈ ÃŽ ³ √3 ≈ 0.58ÃŽ ³. Since ÃŽ ³ ∈ N takes integer values, ÏÆ'ÃŽ ³and ÏÆ' cannot match exactly in general. Moreover, due to the anisotropy of the box filter in 2D, it is impossible to match the covariance matrices. SURF scale parameter analogy Note that box filters are only used to approximate first and second order of Gaussian derivatives in SURF algorithm, and not to approximate Gaussian filtering like in [7]. However, when considering the approximation of second order Gaussian derivative Dxx GÏÆ'(x,y) = Dxx gÏÆ'(x)Ãâ€"gÏÆ'(y) = 1 ÏÆ'22 ÏÆ'2 −1gÏÆ'(x)Ãâ€"gÏÆ'(y) By these condition order box filter operator DLxx, we can see that the1D Gaussian filter gÏÆ'(y) is approximated by 1D box filter with parameter ÃŽ ³ = L−1 2. The authors of SURF claim that the corresponding Gaussian scale is ÏÆ' = 1.2 3 L ≈ 0.8ÃŽ ³for ÃŽ ³ 1, which is close but dià ¯Ã‚ ¬Ã¢â€š ¬erent to the value given by Formula (5): ÏÆ'ÃŽ ³ ≈ 0.58ÃŽ ³. Other analogies could have been made for scale variables, for instance by considering zero crossing of second order derivative of Gaussians, second moment of Gaussian derivatives, mean-square error minimization, but each one provides dià ¯Ã‚ ¬Ã¢â€š ¬erent relations. In conclusion, defining a relation between the box parameters (L and `(L)) and the Gaussian scale variable ÏÆ' seems quite arbitrary. Visual comparison Figure 8 illustrates the dià ¯Ã‚ ¬Ã¢â€š ¬erence between the linear scale-space representation obtained by Gaussian filtering and the box-space, that is its approximation by box-filters when using relation (5). While being roughly similar, the approximated scale-space exhibits some strong vertical and horizontal artifacts due to the anisotropy and the high frequencies of the box kernels. Again, while it is not being used explicitly in SURF, these artifacts may explain some of the spurious detections of the SURF approach that will be exhibited later on. 3.2 Box-space sampling Because of the dentition of first and second order box filters, the size parameter L cannot be chosen arbitrarily. The sampling values and the corresponding variables used to mimic the linear scale space analysis. The following paragraphs give more detailed explanations. Octave decomposition Alike most multi-scale decomposition approaches (see e.g. [13, 15]), the box-space discretization in SURF relies on dyadic sampling of the scale parameter L. The box length representation is therefore divided into octaves (similarly to SIFT [14, 13]), which are indexed by parameter o ∈{1,2,3,4}, where a new octave is created for every doubling of the kernel size. Note that, in order to save computation time, the filtered image is generally sub-sampled of factor two at every octave, as done for instance by SIFT [14]. As pointed out by the author of SURF [2], sub-sampling is not necessary with the use of box filters, since the computation time complexity does not depends on scale. However, while not being explicitly stated in the original paper [2], but as done in most implementations we have reviewed (for instance, this approximation is used in [3] but not in [5]), we choose to use sub-sampling to speed up the algorithm. More precisely, instead of evaluating the multi-scale operators at each pixel, we use a sampling†step† which depends on the octave level (this sampling is detailed in the next sections). Note that this strategy is consistent with the fact that the number of features is decreasing with respect to scale. Level sampling Each octave is also divided in several levels (indexed here by the parameter i ∈ {1,2,3,4}). In the usual discrete scale space analysis, these levels correspond directly to the desired sampling of the scale variable ÏÆ', which parametrizes the discretized Gaussian kernels GÏÆ' (see definition in Eq. (16)). In SURF, the relation between scale L, octave o and level i variables is L := 2o i + 1 . (6) These values are summarized in Table 2. Note that because of the non-maxima suppression involved in the feature selection, only intermediate levels are actually used to define interest points and local descriptors (i ∈{2,3}). On comparison of the box space and the linear scale space. (Top) Convolution with squared and centered box filters with radii ÃŽ ³ = 5 and ÃŽ ³ = 20 (respectively from left to right). (Middle) Corresponding Gaussian filters with respective scales ÏÆ'5 ≈ 3.16 and ÏÆ'20 ≈ 11.83, according to formula (5). Dià ¯Ã‚ ¬Ã¢â€š ¬erence between Gaussian and Box filters (using a linear transform for visualization). We can see here that the box space is a rough approximation of the Gaussian scale space, that exhibits some artifacts due to the anisotropy and the high frequencies of the box kernels. Scale analogy with linear scale space As discussed before in Section 3.1, we can define a scale analysis variable by analogy with the linear scale space decomposition. In [2], the scale parameter ÏÆ'(L) associated with octave o and level i is obtained by the following relation ÏÆ'(L) := 1.2 3(2o Ãâ€"i + 1) = 0.4L. (7) Since the relation between the scale ÏÆ'(L) of an interest point is linear in the size parameter L of box filters operators, we shall speak indià ¯Ã‚ ¬Ã¢â€š ¬erently of the former or the latter to indicate the scale. Remark A finer scale-space representation could be obtained (i.e. with sub-pixel values of L) using a bilinear interpolation of the image, as suggested in [2]. This is not performed in the proposed implementation. 3.3 Comparison with Gaussian derivative operators 3.3.1 First order operators The first order box filters DL x and DL y defined at scale L are approximations of the first derivatives of Gaussian kernel at the corresponding scale ÏÆ'(L) (see Eq. (7)), respectively corresponding to Dx GÏÆ'(x,y) = − x ÏÆ'2(L) GÏÆ'(x,y) and Dy GÏÆ'(x,y). These operators are used for local feature description, in detailed we compares the first order box filter impulse response with the discretized Gaussian derivative kernel. DL x ÃŽ ´ (Eq. (6)) Dx GÏÆ'(L) Illustration of the discrete derivative operator DL x (defined in Section 2.3.1) and discretization of the Gaussian derivative kernel Dx GÏÆ'(L) when using scale relation ÏÆ'(L) from Eq. (7). 3.3.2 The second order operators Second order dià ¯Ã‚ ¬Ã¢â€š ¬erential operators are computed in the scale-space for the detection of interest points [9, 10]. In the linear scale-space representation, this boils down to the convolution with second derivatives of Gaussian kernels Dxx GÏÆ'(x,y) = 1 ÏÆ'22 ÏÆ'2 −1GÏÆ'(x,y), Dyy GÏÆ', and Dxy GÏÆ'(x,y) = xy ÏÆ'4 GÏÆ'(x,y). (8) In the SURF approach, the convolution with theses kernels are approximated by second order box filters, previously introduced respectively as DL xx, DL yy , and DL xy . A visual comparison between second order derivatives of Gaussian and their analogous with box filters. These operators are required for local feature selection step in section 4. 3.3.3 Scale Normalization According to [12], dià ¯Ã‚ ¬Ã¢â€š ¬erential operators have to be normalized when applied in linear scale space in order to achieve scale invariance detection of local features. More precisely, as it can be seen from Equation (21), the amplitude of the continuous second order Gaussian derivative filters decreases with scale variable ÏÆ' by a factor 1 ÏÆ'2. To balance this eà ¯Ã‚ ¬Ã¢â€š ¬ect, second order operators are usually normalized by ÏÆ'2, so that we get for instance (a) (b) (c) (d) On comparison of second order box filters and second order derivative of Gaussian kernels. (a) operator DL yy; (b) discretizedsecondorderGaussianderivative D2 y GÏÆ'; (c) operator DL xy; (d) discretized second order Gaussian derivative Dxy GÏÆ'; For comparison purpose, we used again the scale relation ÏÆ'(L) from Eq. (7). †¢ the scale-normalized determinant of Hessian operator: DoHÏÆ' (u) :=uÏÆ' −(Dxy uÏÆ')2; (9) †¢ the scale-normalized Laplacian operator: à ¢Ã‹â€ Ã¢â‚¬  ÃÆ' u := ÏÆ'2à ¢Ã‹â€ Ã¢â‚¬   uÏÆ' = ÏÆ'2à ¢Ã‹â€ Ã¢â‚¬   GÏÆ' âˆâ€"u = ÏÆ'2(Dxx + Dyy)GÏÆ' âˆâ€"u = ÏÆ'2(Dxx uÏÆ' + Dyy uÏÆ'), (10) where à ¢Ã‹â€ Ã¢â‚¬  ÃÆ' GÏÆ'(x,y) = ÏÆ'2(Dxx +Dyy)à ¢- ¦GÏÆ'(x,y) =x2+y2 ÏÆ'2 −1GÏÆ'(x,y) is the multi-scale Laplacian of Gaussian. Observe that this operator can be obtained from the Trace of the scalenormalized Hessian matrix. These two operators are widely used in computer vision for feature detection. They are also approximatedinSURF,asdetailedinthenextsections. Asaconsequence, suchascale-normalization is also required with box filters to achieve similar invariance in SURF. To do so, the authors proposed that amplitude of operators DL xx , DL yy , and DL xy should be reweighted so that the l2 norms of normalized operators become constant over scales. The quadratic l2 norm of operators are estimated from the squared Frobenius norm of impulse responses kDL xxk2 2 := kDL xx ÃŽ ´k2 F = kDL yy ÃŽ ´k2 F =1 + 1 + (−1)2L(2L−1) = 6L(2L−1), so that kDL xxk2 2 ≈ 12L2 when L=1, and kDL xyk2 2 := kDL xy ÃŽ ´k2 F =1 + 1 + (−1)2 + (−1)2LÃâ€"L = 4L2. This means that box filters responses should be simply divided by the scale parameter L to achieve scale invariance detection. Interest point detection: In the previous sections, second order operators based on box filters have been introduced. These operators are multi-scale and may be normalized to yield scale invariant response. We will now take interest in their use for multi-scale local feature detection. Once the integral image has been computed, three consecutive steps are performed which are detailed in the following sections: 1. Feature filtering based on a combination of second order box filters; 2. Feature selection is combining non-maxima suppression and thresholding; 3. Scale-space location refinement ( § 4.3) using second order interpolation. This interest point detection task is summarized in Algorithm 1. Step-1 Filtering Image by Integration: Integral image and box filters Let u be the processed digital image defined over the pixel grid à ¢Ã¢â‚¬Å¾Ã‚ ¦ = [0,N-1]Ãâ€"[0.M-1], where M and N are positive integers. In the following, we only consider quantized gray valued images (taking values in the range [0; 255]), which is the simplest way to achieve robustness to color modifications, such as a white balance correction. The integral image of I for(x,y) à Ã¢â‚¬Å¾ à ¢Ã¢â‚¬Å¾Ã‚ ¦ is Flow Diagram: Figure3.1: showing the flow chart of the process for object detection Step 2: Point Detection: During the detection step, the local maxima in the box-space of the determinant of Hessian† operator are used to select interest point candidates. These candidates are then validated if the response is above a given threshold. Both the scale and location of these candidates are then refined using quadratic fitting. Typically, a few hundred interest points are detected in a megapixel image. input: image u, integral image U, octave o, level i output: DoHL(u) function Determinant_of_Hessian (U; o; i) L 2oi + 1 (Scale variable, Eq. (19)) for x := 0 to M à ´Ã¢â€š ¬Ã¢â€š ¬Ã¢â€š ¬ 1, step 2oà ´Ã¢â€š ¬Ã¢â€š ¬Ã¢â€š ¬1 do (Loop on columns) for y := 0 to N à ´Ã¢â€š ¬Ã¢â€š ¬Ã¢â€š ¬ 1, step 2oà ´Ã¢â€š ¬Ã¢â€š ¬Ã¢â€š ¬1 do (Loop on rows) DoHL(u)(x; y) Formula (24) (with (4), (10) and (11)) end for end for return DoHL(u) end function Algo input: image u output: listKeyPoints (Initialization) U IntegralImage(u) (Eq. (1)) (Step 1: filtering of features) for L 2 f3; 5; 7; 9; 13; 17; 25; 33; 49; 65g do (scale sampling) DoHL(u) Determinant_of_Hessian (U; L) end for (Step 2: selection and refinement of keypoints) for o := 1 to 4 do (octave sampling) for i := 2 to 3 do (levels sampling for maxima location) L -> 2o i + 1 listKeyPoints -> listKeyPoints + KeyPoints(o; i;DoHL(u)) end for end for return listKeyPoints So that the scale normalization factor C(L) for second order box filters should be proportional to 1 L2 However, the previous normalization is only true when L1. Indeed, while we have kDxxGÏÆ'k2 2 kDxyGÏÆ'k2 2 = 3 at any scale ÏÆ', this is not exactly true with box filters, where: kDL xxk2 2 kDL xyk2 2 = 3(2L−1) 2L ≈ 3 when L1. To account for this dià ¯Ã‚ ¬Ã¢â€š ¬erence in normalization for small scales, while keeping the same (fast) un-normalized box filters, the author of SURF introduced in Formula (24) a weight factor: w(L) = kDL xxk2 kDL xyk2  ·kDxyGÏÆ'k2 kDxxGÏÆ'k2 =r2L−1 2L . (26) The numerical values of this parameter are listed in the last column of Table 2. As noticed by the authors of SURF, the variable w(L) does not vary so much across scales. This is the resaon why the weighting parameter w in Eq. (10) is fixed to w(3) = 0.9129. Feature selection: In our methodology, interest points are defined as local maxima of the aforementioned DoHL operator applied to the image u. These maxima are detected by considering a 3 Ãâ€" 3 Ãâ€" 3 neighborhood, andperforminganexhaustivecomparisonofeveryvoxelofthediscretebox-spacewith its 26 nearest-neighbors. The corresponding feature selection procedure is described in Algorithm 3. Algorithm 3 Selection of features input: o,i,DoHL(u) (Determinant of Hessian response at octave o and level i) output: listKeyPoints (List of keypoints in box space with sub-pixel coordinates (x,y,L)) function KeyPoints (o,i,DoHL(u)) L ↠ 2oi + 1 for x := 0 to M −1, step 2o−1 do (Loop on columns) for y = 0 to N −1, step 2o−1 do (Loop on rows) if DoHL(u)(x,y) > tH then (Thresholding) if isMaximum (DoHL(u),x,y) then (Non-maximum suppression) if isRefined (DoHL(u),x,y,L) then addListKeyPoints (x,y,L) end if end if end if end for end for return listKeyPoints end function Remark A faster method has been proposed in [21] to find the local maxima without exhaustive search, which has been not implemented for the demo. Thresholding: Using four octaves and two levels for analysis, eight dià ¯Ã‚ ¬Ã¢â€š ¬erent scales are therefore analyzed (see Table 2 in Section 3.2). In order to obtain a compact representation of the image -and also to cope with noise perturbation- the algorithm selects the most salient features from this set of local maxima. This is achieved by using a threshold tH on the response of the DoHL operator DoHL(u)(x,y) > tH . (27) Note that, since the operator is scale-normalized, the threshold is constant. In the demo, this threshold has been set to 10 assuming that the input image u takes values in the intervalJ0,255K. This setting enables us to have a performance similar to the original SURF algorithm [2, 1] (see Section 6 for more details). Figure 13 shows the set of interest points detected as local box-space maxima of the DoHL operator, and selected after thresholding. For visualization purpose, the radii of the circles is set as 2.5 times the box scale L of the corresponding interest points.

Terrorisms Effects on the World Essay -- Terrorism Violence Ethnicity

Terrorism's Effects on the World Ethnic conflicts arise everyday among people in society. Although problems between the populace have changed in the present generation, ethnic issues have been apparent since the beginning of time. Some of the modern cultural conflicts can range from terrorism to religious wars. Terrorism has been a predicament throughout the entire world ever since the day man was created. It seems to be the answer to many leaders’ issues with â€Å"foreign† people. Not only is terrorism harmful itself in many ways, it causes new problems that worsen every situation. For example, terrorism is presently forcing many countries around the world, including the United States, to change their life styles, political processes, and even their economic values as a reaction to the threats.   Ã‚  Ã‚  Ã‚  Ã‚  Although terrorism begins in one particular area, it seems to spread to other regions as well, even though not exactly to the same extent. For example, in the United States, there were consecutive bombings and suicide attacks during a short period of time. Considering that this causes paranoia, other countries began to create new laws and restrictions in order to protect its people, even though the attacks weren’t necessarily focused towards that specific region. As an immediate response to the terrorism, the United States government changed in many ways, from restricting the citizens’ rights, to ending certain public activities, up to restricting entrance into certain buildings. The government even began to limit the media. Similarly to the United States, on September 22nd, the Russian parliament was debating on which possible new restrictions and laws can be enforced in order to â€Å"guarantee† better national security. New laws in the United States, such as the right to declare a â€Å"state of war ,† make the country seem as if it has more power than neighboring areas, helping to comfort any fearful, or paranoid, citizens. Terrorism, as well as other ethnic conflicts, has even caused the people of Asia-Pacific countries to create new laws in reaction to the threats, simply because they became intimidated. An article in the Financial times stated that it is trying to boost their â€Å"political momentum† behind the battle of terrorism, promising to take practical steps to improve their co-operation . New laws were being considered, due to the fear and security levels o... ...ked and used as hideouts. For example, Russia, South America, and certain sections of the United States are simple targets because of the land and the population densities, unlike places such as the Sahara Desert. Of course, if a terrorist truly wanted to mass murder a group, they would simply set off a nuclear explosion. This would obviously create a complete new set of problems along with those that we have already discussed. Personally, I believe wars shouldn’t be encouraged, and honestly, I don’t suppose that it results in any positive feedback whatsoever. Although I don’t completely agree with how Russia, Indonesia, South America, and Darfur are dealing with their terrorist problem, I still consider it a good idea to think forward and make plans on how to overcome cultural problems. Terrorism has caused many countries around the world, to change their daily values and their economic priorities, as well as their whole political framework as a response to ethnic conflicts. Terrorism has been a common answer among leaders regarding intercultural problems among â€Å"different† people ever since mankind was created. Ethnic conflict is now becoming a new, everyday aspect of life.

A Game of Thrones Chapter Forty-two

Tyrion They had taken shelter beneath a copse of aspens just off the high road. Tyrion was gathering deadwood while their horses took water from a mountain stream. He stooped to pick up a splintered branch and examined it critically. â€Å"Will this do? I am not practiced at starting fires. Morrec did that for me.† â€Å"A fire?† Bronn said, spitting. â€Å"Are you so hungry to die, dwarf? Or have you taken leave of your senses? A fire will bring the clansmen down on us from miles around. I mean to survive this journey, Lannister.† â€Å"And how do you hope to do that?† Tyrion asked. He tucked the branch under his arm and poked around through the sparse undergrowth, looking for more. His back ached from the effort of bending; they had been riding since daybreak, when a stone-faced Ser Lyn Corbray had ushered them through the Bloody Gate and commanded them never to return. â€Å"We have no chance of fighting our way back,† Bronn said, â€Å"but two can cover more ground than ten, and attract less notice. The fewer days we spend in these mountains, the more like we are to reach the riverlands. Ride hard and fast, I say. Travel by night and hole up by day, avoid the road where we can, make no noise and light no fires.† Tyrion Lannister sighed. â€Å"A splendid plan, Bronn. Try it, as you like . . . and forgive me if I do not linger to bury you.† â€Å"You think to outlive me, dwarf?† The sellsword grinned. He had a dark gap in his smile where the edge of Ser Vardis Egen's shield had cracked a tooth in half. Tyrion shrugged. â€Å"Riding hard and fast by night is a sure way to tumble down a mountain and crack your skull. I prefer to make my crossing slow and easy. I know you love the taste of horse, Bronn, but if our mounts die under us this time, we'll be trying to saddle shadowcats . . . and if truth be told, I think the clans will find us no matter what we do. Their eyes are all around us.† He swept a gloved hand over the high, wind-carved crags that surrounded them. Bronn grimaced. â€Å"Then we're dead men, Lannister.† â€Å"If so, I prefer to die comfortable,† Tyrion replied. â€Å"We need a fire. The nights are cold up here, and hot food will warm our bellies and lift our spirits. Do you suppose there's any game to be had? Lady Lysa has kindly provided us with a veritable feast of salt beef, hard cheese, and stale bread, but I would hate to break a tooth so far from the nearest maester.† â€Å"I can find meat.† Beneath a fall of black hair, Bronn's dark eyes regarded Tyrion suspiciously. â€Å"I should leave you here with your fool's fire. If I took your horse, I'd have twice the chance to make it through. What would you do then, dwarf?† â€Å"Die, most like.† Tyrion stooped to get another stick. â€Å"You don't think I'd do it?† â€Å"You'd do it in an instant, if it meant your life. You were quick enough to silence your friend Chiggen when he caught that arrow in his belly.† Bronn had yanked back the man's head by the hair and driven the point of his dirk in under the ear, and afterward told Catelyn Stark that the other sellsword had died of his wound. â€Å"He was good as dead,† Bronn said, â€Å"and his moaning was bringing them down on us. Chiggen would have done the same for me . . . and he was no friend, only a man I rode with. Make no mistake, dwarf. I fought for you, but I do not love you.† â€Å"It was your blade I needed,† Tyrion said, â€Å"not your love.† He dumped his armful of wood on the ground. Bronn grinned. â€Å"You're bold as any sellsword, I'll give you that. How did you know I'd take your part?† â€Å"Know?† Tyrion squatted awkwardly on his stunted legs to build the fire. â€Å"I tossed the dice. Back at the inn, you and Chiggen helped take me captive. Why? The others saw it as their duty, for the honor of the lords they served, but not you two. You had no lord, no duty, and precious little honor, so why trouble to involve yourselves?† He took out his knife and whittled some thin strips of bark off one of the sticks he'd gathered, to serve as kindling. â€Å"Well, why do sellswords do anything? For gold. You were thinking Lady Catelyn would reward you for your help, perhaps even take you into her service. Here, that should do, I hope. Do you have a flint?† Bronn slid two fingers into the pouch at his belt and tossed down a flint. Tyrion caught it in the air. â€Å"My thanks,† he said. â€Å"The thing is, you did not know the Starks. Lord Eddard is a proud, honorable, and honest man, and his lady wife is worse. Oh, no doubt she would have found a coin or two for you when this was all over, and pressed it in your hand with a polite word and a look of distaste, but that's the most you could have hoped for. The Starks look for courage and loyalty and honor in the men they choose to serve them, and if truth be told, you and Chiggen were lowborn scum.† Tyrion struck the flint against his dagger, trying for a spark. Nothing. Bronn snorted. â€Å"You have a bold tongue, little man. One day someone is like to cut it out and make you eat it.† â€Å"Everyone tells me that.† Tyrion glanced up at the sellsword. â€Å"Did I offend you? My pardons . . . but you are scum, Bronn, make no mistake. Duty, honor, friendship, what's that to you? No, don't trouble yourself, we both know the answer. Still, you're not stupid. Once we reached the Vale, Lady Stark had no more need of you . . . but I did, and the one thing the Lannisters have never lacked for is gold. When the moment came to toss the dice, I was counting on your being smart enough to know where your best interest lay. Happily for me, you did.† He slammed stone and steel together again, fruitlessly. â€Å"Here,† said Bronn, squatting, â€Å"I'll do it.† He took the knife and flint from Tyrion's hands and struck sparks on his first try. A curl of bark began to smolder. â€Å"Well done,† Tyrion said. â€Å"Scum you may be, but you're undeniably useful, and with a sword in your hand you're almost as good as my brother Jaime. What do you want, Bronn? Gold? Land? Women? Keep me alive, and you'll have it.† Bronn blew gently on the fire, and the flames leapt up higher. â€Å"And if you die?† â€Å"Why then, I'll have one mourner whose grief is sincere,† Tyrion said, grinning. â€Å"The gold ends when I do.† The fire was blazing up nicely. Bronn stood, tucked the flint back into his pouch, and tossed Tyrion his dagger. â€Å"Fair enough,† he said. â€Å"My sword's yours, then . . . but don't go looking for me to bend the knee and m'lord you every time you take a shit. I'm no man's toady.† â€Å"Nor any man's friend,† Tyrion said. â€Å"I've no doubt you'd betray me as quick as you did Lady Stark, if you saw a profit in it. If the day ever comes when you're tempted to sell me out, remember this, Bronn—I'll match their price, whatever it is. I like living. And now, do you think you could do something about finding us some supper?† â€Å"Take care of the horses,† Bronn said, unsheathing the long dirk he wore at his hip. He strode into the trees. An hour later the horses had been rubbed down and fed, the fire was crackling away merrily, and a haunch of a young goat was turning above the flames, spitting and hissing. â€Å"All we lack now is some good wine to wash down our kid,† Tyrion said. â€Å"That, a woman, and another dozen swords,† Bronn said. He sat cross-legged beside the fire, honing the edge of his longsword with an oilstone. There was something strangely reassuring about the rasping sound it made when he drew it down the steel. â€Å"It will be full dark soon,† the sellsword pointed out. â€Å"I'll take first watch . . . for all the good it will do us. It might be kinder to let them kill us in our sleep.† â€Å"Oh, I imagine they'll be here long before it comes to sleep.† The smell of the roasting meat made Tyrion's mouth water. Bronn watched him across the fire. â€Å"You have a plan,† he said flatly, with a scrape of steel on stone. â€Å"A hope, call it,† Tyrion said. â€Å"Another toss of the dice.† â€Å"With our lives as the stake?† Tyrion shrugged. â€Å"What choice do we have?† He leaned over the fire and sawed a thin slice of meat from the kid. â€Å"Ahhhh,† he sighed happily as he chewed. Grease ran down his chin. â€Å"A bit tougher than I'd like, and in want of spicing, but I'll not complain too loudly. If I were back at the Eyrie, I'd be dancing on a precipice in hopes of a boiled bean.† â€Å"And yet you gave the turnkey a purse of gold,† Bronn said. â€Å"A Lannister always pays his debts.† Even Mord had scarcely believed it when Tyrion tossed him the leather purse. The gaoler's eyes had gone big as boiled eggs as he yanked open the drawstring and beheld the glint of gold. â€Å"I kept the silver,† Tyrion had told him with a crooked smile, â€Å"but you were promised the gold, and there it is.† It was more than a man like Mord could hope to earn in a lifetime of abusing prisoners. â€Å"And remember what I said, this is only a taste. If you ever grow tired of Lady Arryn's service, present yourself at Casterly Rock, and I'll pay you the rest of what I owe you.† With golden dragons spilling out of both hands, Mord had fallen to his knees and promised that he would do just that. Bronn yanked out his dirk and pulled the meat from the fire. He began to carve thick chunks of charred meat off the bone as Tyrion hollowed out two heels of stale bread to serve as trenchers. â€Å"If we do reach the river, what will you do then?† the sellsword asked as he cut. â€Å"Oh, a whore and a featherbed and a flagon of wine, for a start.† Tyrion held out his trencher, and Bronn filled it with meat. â€Å"And then to Casterly Rock or King's Landing, I think. I have some questions that want answering, concerning a certain dagger.† The sellsword chewed and swallowed. â€Å"So you were telling it true? It was not your knife?† Tyrion smiled thinly. â€Å"Do I look a liar to you?† By the time their bellies were full, the stars had come out and a halfmoon was rising over the mountains. Tyrion spread his shadowskin cloak on the ground and stretched out with his saddle for a pillow. â€Å"Our friends are taking their sweet time.† â€Å"If I were them, I'd fear a trap,† Bronn said. â€Å"Why else would we be so open, if not to lure them in?† Tyrion chuckled. â€Å"Then we ought to sing and send them fleeing in terror.† He began to whistle a tune. â€Å"You're mad, dwarf,† Bronn said as he cleaned the grease out from under his nails with his dirk. â€Å"Where's your love of music, Bronn?† â€Å"If it was music you wanted, you should have gotten the singer to champion you.† Tyrion grinned. â€Å"That would have been amusing. I can just see him fending off Ser Vardis with his woodharp.† He resumed his whistling. â€Å"Do you know this song?† he asked. â€Å"You hear it here and there, in inns and whorehouses.† â€Å"Myrish. ‘The Seasons of My Love.' Sweet and sad, if you understand the words. The first girl I ever bedded used to sing it, and I've never been able to put it out of my head.† Tyrion gazed up at the sky. It was a clear cold night and the stars shone down upon the mountains as bright and merciless as truth. â€Å"I met her on a night like this,† he heard himself saying. â€Å"Jaime and I were riding back from Lannisport when we heard a scream, and she came running out into the road with two men dogging her heels, shouting threats. My brother unsheathed his sword and went after them, while I dismounted to protect the girl. She was scarcely a year older than I was, dark-haired, slender, with a face that would break your heart. It certainly broke mine. Lowborn, half-starved, unwashed . . . yet lovely. They'd torn the rags she was wearing half off her back, so I wrapped her in my cloak while Jaime chased the men into the woods. By the time he came trotting back, I'd gotten a name out of her, and a story. She was a crofter's child, orphaned when her father died of fever, on her way to . . . well, nowhere, really. â€Å"Jaime was all in a lather to hunt down the men. It was not often outlaws dared prey on travelers so near to Casterly Rock, and he took it as an insult. The girl was too frightened to send off by herself, though, so I offered to take her to the closest inn and feed her while my brother rode back to the Rock for help. â€Å"She was hungrier than I would have believed. We finished two whole chickens and part of a third, and drank a flagon of wine, talking. I was only thirteen, and the wine went to my head, I fear. The next thing I knew, I was sharing her bed. If she was shy, I was shyer. I'll never know where I found the courage. When I broke her maidenhead, she wept, but afterward she kissed me and sang her little song, and by morning I was in love.† â€Å"You?† Bronn's voice was amused. â€Å"Absurd, isn't it?† Tyrion began to whistle the song again. â€Å"I married her,† he finally admitted. â€Å"A Lannister of Casterly Rock wed to a crofter's daughter,† Bronn said. â€Å"How did you manage that?† â€Å"Oh, you'd be astonished at what a boy can make of a few lies, fifty pieces of silver, and a drunken septon. I dared not bring my bride home to Casterly Rock, so I set her up in a cottage of her own, and for a fortnight we played at being man and wife. And then the septon sobered and confessed all to my lord father.† Tyrion was surprised at how desolate it made him feel to say it, even after all these years. Perhaps he was just tired. â€Å"That was the end of my marriage.† He sat up and stared at the dying fire, blinking at the light. â€Å"He sent the girl away?† â€Å"He did better than that,† Tyrion said. â€Å"First he made my brother tell me the truth. The girl was a whore, you see. Jaime arranged the whole affair, the road, the outlaws, all of it. He thought it was time I had a woman. He paid double for a maiden, knowing it would be my first time. â€Å"After Jaime had made his confession, to drive home the lesson, Lord Tywin brought my wife in and gave her to his guards. They paid her fair enough. A silver for each man, how many whores command that high a price? He sat me down in the corner of the barracks and bade me watch, and at the end she had so many silvers the coins were slipping through her fingers and rolling on the floor, she . . . † The smoke was stinging his eyes. Tyrion cleared his throat and turned away from the fire, to gaze out into darkness. â€Å"Lord Tywin had me go last,† he said in a quiet voice. â€Å"And he gave me a gold coin to pay her, because I was a Lannister, and worth more.† After a time he heard the noise again, the rasp of steel on stone as Bronn sharpened his sword. â€Å"Thirteen or thirty or three, I would have killed the man who did that to me.† Tyrion swung around to face him. â€Å"You may get that chance one day. Remember what I told you. A Lannister always pays his debts.† He yawned. â€Å"I think I will try and sleep. Wake me if we're about to die.† He rolled himself up in the shadowskin and shut his eyes. The ground was stony and cold, but after a time Tyrion Lannister did sleep. He dreamt of the sky cell. This time he was the gaoler, not the prisoner, big, with a strap in his hand, and he was hitting his father, driving him back, toward the abyss . . . â€Å"Tyrion.† Bronn's warning was low and urgent. Tyrion was awake in the blink of an eye. The fire had burned down to embers, and the shadows were creeping in all around them. Bronn had raised himself to one knee, his sword in one hand and his dirk in the other. Tyrion held up a hand: stay still, it said. â€Å"Come share our fire, the night is cold,† he called out to the creeping shadows. â€Å"I fear we've no wine to offer you, but you're welcome to some of our goat.† All movement stopped. Tyrion saw the glint of moonlight on metal. â€Å"Our mountain,† a voice called out from the trees, deep and hard and unfriendly. â€Å"Our goat.† â€Å"Your goat,† Tyrion agreed. â€Å"Who are you?† â€Å"When you meet your gods,† a different voice replied, â€Å"say it was Gunthor son of Gurn of the Stone Crows who sent you to them.† A branch cracked underfoot as he stepped into the light; a thin man in a horned helmet, armed with a long knife. â€Å"And Shagga son of Dolf.† That was the first voice, deep and deadly. A boulder shifted to their left, and stood, and became a man. Massive and slow and strong he seemed, dressed all in skins, with a club in his right hand and an axe in his left. He smashed them together as he lumbered closer. Other voices called other names, Conn and Torrek and Jaggot and more that Tyrion forgot the instant he heard them; ten at least. A few had swords and knives; others brandished pitchforks and scythes and wooden spears. He waited until they were done shouting out their names before he gave them answer. â€Å"I am Tyrion son of Tywin, of the Clan Lannister, the Lions of the Rock. We will gladly pay you for the goat we ate.† â€Å"What do you have to give us, Tyrion son of Tywin?† asked the one who named himself Gunthor, who seemed to be their chief. â€Å"There is silver in my purse,† Tyrion told them. â€Å"This hauberk I wear is large for me, but it should fit Conn nicely, and the battle-axe I carry would suit Shagga's mighty hand far better than that wood-axe he holds.† â€Å"The halfman would pay us with our own coin,† said Conn. â€Å"Conn speaks truly,† Gunthor said. â€Å"Your silver is ours. Your horses are ours. Your hauberk and your battle-axe and the knife at your belt, those are ours too. You have nothing to give us but your lives. How would you like to die, Tyrion son of Tywin?† â€Å"In my own bed, with a belly full of wine and a maiden's mouth around my cock, at the age of eighty,† he replied. The huge one, Shagga, laughed first and loudest. The others seemed less amused. â€Å"Conn, take their horses,† Gunthor commanded. â€Å"Kill the other and seize the halfinan. He can milk the goats and make the mothers laugh.† Bronn sprang to his feet. â€Å"Who dies first?† â€Å"No!† Tyrion said sharply. â€Å"Gunthor son of Gurn, hear me. My House is rich and powerful. If the Stone Crows will see us safely through these mountains, my lord father will shower you with gold.† â€Å"The gold of a lowland lord is as worthless as a halfman's promises,† Gunthor said. â€Å"Half a man I may be,† Tyrion said, â€Å"yet I have the courage to face my enemies. What do the Stone Crows do, but hide behind rocks and shiver with fear as the knights of the Vale ride by?† Shagga gave a roar of anger and clashed club against axe. Jaggot poked at Tyrion's face with the fire-hardened point of a long wooden spear. He did his best not to flinch. â€Å"Are these the best weapons you could steal?† he said. â€Å"Good enough for killing sheep, perhaps . . . if the sheep do not fight back. My father's smiths shit better steel.† â€Å"Little boyman,† Shagga roared, â€Å"will you mock my axe after I chop off your manhood and feed it to the goats?† But Gunthor raised a hand. â€Å"No. I would hear his words. The mothers go hungry, and steel fills more mouths than gold. What would you give us for your lives, Tyrion son of Tywin? Swords? Lances? Mail?† â€Å"All that, and more, Gunthor son of Gurn,† Tyrion Lannister replied, smiling. â€Å"I will give you the Vale of Arryn.†

Lady Macbeth, the three witches and Macbeth Essay

The play was set in 1040 in Scotland; it deals with the issues in his life that is still relevant today in order to fulfil his emission. Macbeth starts out as a national hero and co leader of the Scottish army, he quickly gains popularity. Until one day he receives a prophecy, which turns him on an evil rampage. Where he sets off in a killing spree to try and become king. However he has doubts and maybe even some regrets about his choices. It seems to mess with hiss head for example after he killed Duncan he keeps seeing what he thinks is his ghost, is he sane? Other characters include Lady Macbeth, the three witches, Banquo, Duncan, Malcolm and Mac duff. In total there are 32 characters. Lady Macbeth is a very strong minded character. She can be very ruthless at times; she is very supportive of her husband but can also push him into things. She has a strong influence on him when committing several murders. She knows what needs to be done to get the job done. This would be very different from the typical Shakespearian woman who would have been very quiet and little power over their husbands her opening soliloquy introduces her as a very strong character that will stand up and fight for what she believes in. If it had not been for lady Macbeth the murder of Duncan may not have went ahead as it was mainly her who put him up to it. She also went back for the daggers that he used to murder him so this stopped him getting caught, this shows that she doesn’t want any harm to come to her husband and she clearly must love him. However what she had turned Macbeth into would be his down fall. She had turned him into something he had never wanted to become, a cold blooded murder. Back in Shakespearian people were very scared and believed fully in the supernatural. They would have heard stories of these sorts of things so when they heard of the three witches telling prophecies they would have been wary. The appearance and strange characteristics of the witches also added an effect. Their clothing added a 3rd dimension to their character, the way they are descried â€Å"the ripped old dirty rags†. Their skinny bodies, cold withered faces add to the disturbing image. All in all not the sort of people you want to meet in a dark alley. When they told Macbeth that he would become king if he killed Duncan, this had a very strong influence on Macbeth. If it had not been for them he would have never thought twice about killing him. The help of the witches soon led to many regrets â€Å"neither man nor woman can harm him† said the witches. The witches may have liked him all along; this is why they told him their predictions. Macbeth’s character changes dramatically through the play, so also do peoples views of him. In the beginning he is introduced as a highly respected general, a hero! Soon becomes easily scared and persuaded into bad things. I think this show how the pursuit for power can destroy your life. Macbeth had a great position and lots of respect but it wasn’t enough. He gave every thing he had to pursue being king and look here it got him, committed terrible deeds and the people just lost all respect for him.